lme4/0000755000176200001440000000000015113602772011117 5ustar liggesuserslme4/tests/0000755000176200001440000000000015113144725012260 5ustar liggesuserslme4/tests/glmmWeights.R0000644000176200001440000000741615022107260014672 0ustar liggesusersif (.Platform$OS.type != "windows") { library(lme4) library(testthat) source(system.file("testdata/lme-tst-funs.R", package="lme4", mustWork=TRUE)) ##-> gSim(), a general simulation function ... ## hand-coded Pearson residuals {for sumFun() } mypresid <- function(x) { mu <- fitted(x) (getME(x,"y") - mu) * sqrt(weights(x)) / sqrt(x@resp$family$variance(mu)) } ## should be equal (up to numerical error) to weights(.,type="working") workingWeights <- function(mod) mod@resp$weights*(mod@resp$muEta()^2)/mod@resp$variance() ##' Sum of weighted residuals, 4 ways; the last three are identical sumFun <- function(m) { wrss1 <- m@devcomp$cmp["wrss"] wrss2 <- sum(residuals(m,type="pearson")^2) wrss3 <- sum(m@resp$wtres^2) ## compare to hand-fitted Pearson resids ... wrss4 <- sum(mypresid(m)^2) c(wrss1,wrss2,wrss3,wrss4) } ## The relative "error"/differences of the weights w[] entries rel.diff <- function(w) abs(1 - w[-1]/w[1]) set.seed(101) ## GAMMA g0 <- glmer(y~x+(1|block),data=gSim(),family=Gamma) expect_true(all(rel.diff(sumFun(g0)) < 1e-13)) expect_equal(weights(g0, type = "working"), workingWeights(g0), tolerance = 1e-4) ## FIXME: why is such a high tolerance required? ## BERNOULLI g1 <- glmer(y~x+(1|block),data=gSim(family=binomial(),nbinom=1), family=binomial) expect_true(all(rel.diff(sumFun(g1)) < 1e-13)) expect_equal(weights(g1, type = "working"), workingWeights(g1), tolerance = 1e-5) ## FIXME: why is such a high tolerance required? ## POISSON (n <- nrow(d.P <- gSim(family=poisson()))) g2 <- glmer(y ~ x + (1|block), data = d.P, family=poisson) g2W <- glmer(y ~ x + (1|block), data = d.P, family=poisson, weights = rep(2,n)) expect_true(all(rel.diff(sumFun(g2 )) < 1e-13)) expect_true(all(rel.diff(sumFun(g2W)) < 1e-13)) ## correct expect_equal(weights(g2, type = "working"), workingWeights(g2), tolerance = 1e-5) ## FIXME: why is such a high tolerance required? expect_equal(weights(g2W, type = "working"), workingWeights(g2W), tolerance = 1e-5) ## FIXME: why is such a high tolerance required? ## non-Bernoulli BINOMIAL g3 <- glmer(y ~ x + (1|block), data= gSim(family=binomial(), nbinom=10), family=binomial) expect_true(all(rel.diff(sumFun(g3)) < 1e-13)) expect_equal(weights(g3, type = "working"), workingWeights(g3), tolerance = 1e-4) ## FIXME: why is such a high tolerance required? d.b.2 <- gSim(nperblk = 2, family=binomial()) g.b.2 <- glmer(y ~ x + (1|block), data=d.b.2, family=binomial) expect_true(all(rel.diff(sumFun(g.b.2 )) < 1e-13)) ## Many blocks of only 2 observations each - (but nicely balanced) ## Want this "as" https://github.com/lme4/lme4/issues/47 ## (but it "FAILS" survival already): ## ## n2 = n/2 : n2 <- 2048 if(FALSE) n2 <- 100 # for building/testing set.seed(47) dB2 <- gSim(n2, nperblk = 2, x= rep(0:1, each= n2), family=binomial()) ## -- -- --- -------- gB2 <- glmer(y ~ x + (1|block), data=dB2, family=binomial) expect_true(all(rel.diff(sumFun(gB2)) < 1e-13)) ## NB: Finite sample bias of \hat\sigma_1 and \hat\beta_1 ("Intercept") ## tend to zero only slowly for n2 -> Inf, e.g., for ## n2 = 2048, b1 ~= 4.3 (instead of 4); s1 ~= 1.3 (instead of 1) ## FAILS ----- ## library(survival) ## (gSurv.B2 <- clogit(y ~ x + strata(block), data=dB2)) ## ## --> Error in Surv(rep(1, 200L), y) : Time and status are different lengths ## summary(gSurv.B2) ## (SE.surf <- sqrt(diag(vcov(gSurv.B2)))) g3 <- glmer(y ~ x + (1|block),data=gSim(family=binomial(),nbinom=10), family=binomial) expect_equal(var(sumFun(g3)),0) ## check dispersion parameter ## (lowered tolerance to pass checks on my machine -- SCW) expect_equal(sigma(g0)^2, 0.4888248, tolerance=1e-4) } ## skip on windows (for speed) lme4/tests/dynload.R0000644000176200001440000000367114677066752014065 0ustar liggesusers## this is the simpler version of the code for testing/exercising ## https://github.com/lme4/lme4/issues/35 ## see also ../misc/issues/dynload.R for more complexity pkg <- so_name <- "lme4"; doUnload <- FALSE; doTest <- TRUE ## pkg <- so_name <- "RcppEigen"; doUnload <- TRUE; doTest <- TRUE ## need to deal with the fact that DLL name != package name for lme4.0 ... ### pkg <- "lme4.0"; so_name <- "lme4"; doUnload <- TRUE instPkgs <- as.data.frame(installed.packages(),stringsAsFactors=FALSE) Load <- function() { library(pkg,character.only=TRUE) } Unload <- function() { ld <- library.dynam() pnames <- sapply(ld,"[[","name") names(ld) <- pnames lp <- gsub("/libs/.*$","",ld[[so_name]][["path"]]) cat("unloading from",lp,"\n") library.dynam.unload(so_name, lp) } Detach <- function() { detach(paste0("package:",pkg),character.only=TRUE,unload=TRUE) if (doUnload) Unload() } tmpf <- function() { g <- getLoadedDLLs() lnames <- names(g)[is.na(instPkgs[names(g),"Priority"])] cat("loaded DLLs:",lnames,"\n") g <- g[na.omit(match(c(so_name,"nlme"),names(g)))] class(g) <- "DLLInfoList" g } test <- function() { if (doTest) { if (pkg %in% c("lme4","lme4.0")) { fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, devFunOnly=TRUE) } if (pkg=="RcppEigen") { data(trees, package="datasets") mm <- cbind(1, log(trees$Girth)) # model matrix y <- log(trees$Volume) # response ## bare-bones direct interface flm <- fastLmPure(mm, y) } } } if (FALSE) { ## FIXME: disabled test for now for (i in 1:6) { cat("Attempt #",i,"\n",sep="") cat("loading",pkg,"\n") Load() tmpf() test() cat("detaching",pkg,"\n") Detach() cat("loading nlme\n") library("nlme") tmpf() detach("package:nlme",unload=TRUE) cat("detaching nlme\n") } } lme4/tests/nbinom.R0000644000176200001440000001513115103163201013653 0ustar liggesusersif (.Platform$OS.type != "windows") { library(lme4) cat("lme4 testing level: ", testLevel <- lme4:::testLevel(), "\n") getNBdisp <- function(x) getME(x,"glmer.nb.theta") ## for now, use hidden functions [MM: this is a sign, we should *export* them] refitNB <- lme4:::refitNB simfun <- function(sd.u=1, NBtheta=0.5, nblock = 25, fform = ~x, beta = c(1,2), nrep = 40, seed) { levelset <- c(LETTERS,letters) stopifnot(2 <= nblock, nblock <= length(levelset)) if (!missing(seed)) set.seed(seed) ntot <- nblock*nrep d1 <- data.frame(x = runif(ntot), f = factor(rep(levelset[1:nblock], each=nrep))) u_f <- rnorm(nblock, sd=sd.u) X <- model.matrix(fform, data=d1) transform(d1, z = rnbinom(ntot, mu = exp(X %*% beta + u_f[f]), size = NBtheta)) } ##' simplified logLik() so we can compare with "glmmADMB" (and other) results logLik.m <- function(x) { L <- logLik(x) attributes(L) <- attributes(L)[c("class","df","nobs")] L } if (testLevel > 1) withAutoprint({ set.seed(102) d.1 <- simfun() t1 <- system.time(g1 <- glmer.nb(z ~ x + (1|f), data=d.1, verbose=TRUE)) g1 d1 <- getNBdisp(g1) (g1B <- refitNB(g1, theta = d1)) (ddev <- deviance(g1) - deviance(g1B)) (reld <- (fixef(g1) - fixef(g1B)) / fixef(g1)) stopifnot(abs(ddev) < 1e-6, # was 6.18e-7, 1.045e-6, -6.367e-5, now 0 abs(reld) < 1e-6)# 0, then 4.63e-6, now 0 ## 2 Aug 2015: ddev==reld==0 on 32-bit Ubuntu 12.04 if(FALSE) { ## comment out to avoid R CMD check warning : ## library(glmmADMB) t2 <- system.time(g2 <- glmmadmb(z~x+(1|f), data = d.1, family="nbinom")) ## matrix not pos definite in sparse choleski t2 # 17.1 sec elapsed glmmADMB_vals <- list(fixef= fixef(g2), LL = logLik(g2), theta= g2$alpha) } else { glmmADMB_vals <- list(fixef = c("(Intercept)" = 0.928710, x = 2.05072), LL = structure(-2944.62, class = "logLik", df = 4, nobs = 1000L), theta = 0.4487) } stopifnot(exprs = { all.equal( d1, glmmADMB_vals$ theta, tolerance=0.003) # 0.0015907 all.equal(fixef(g1B), glmmADMB_vals$ fixef, tolerance=0.02)# was 0.009387 ! ## Ubuntu 12.04/32-bit: 0.0094 all.equal(logLik.m(g1B), glmmADMB_vals$ LL, tolerance=1e-4)# 1.681e-5; Ubuntu 12.04/32-b: 1.61e-5 }) })## end if( testLevel > 1 ) if(FALSE) { ## simulation study -------------------- ## library(glmmADMB) ## avoid R CMD check warning simsumfun <- function(...) { d <- simfun(...) t1 <- system.time(g1 <- glmer.nb(z~x+(1|f),data=d)) t2 <- system.time(g2 <- glmmadmb(z~x+(1|f), data=d,family="nbinom")) c(t.glmer=unname(t1["elapsed"]),nevals.glmer=g1$nevals, theta.glmer=exp(g1$minimum), t.glmmadmb=unname(t2["elapsed"]),theta.glmmadmb=g2$alpha) } ## library(plyr) ## sim50 <- raply(50,simsumfun(),.progress="text") save("sim50",file="nbinomsim1.RData") ## library(reshape) ## m1 <- melt(data.frame(run=seq(nrow(sim50)),sim50),id.var="run") ## m1 <- data.frame(m1,colsplit(m1$variable,"\\.",c("v","method"))) ## m2 <- cast(subset(m1,v=="theta",select=c(run,value,method)), ## run~method) library(ggplot2) ggplot(subset(m1,v=="theta"),aes(x=method,y=value))+ geom_boxplot()+geom_point()+geom_hline(yintercept=0.5,colour="red") ggplot(subset(m1,v=="theta"),aes(x=method,y=value))+ stat_summary(fun.data=mean_cl_normal)+ geom_hline(yintercept=0.5,colour="red") ggplot(m2,aes(x=glmer-glmmadmb))+geom_histogram() ## glmer is slightly more biased (but maybe the MLE itself is biased???) }## end{simulation study}------------------------- ### epilepsy example: data(epil, package="MASS") epil2 <- transform(epil, Visit = (period-2.5)/5, Base = log(base/4), Age = log(age), subject= factor(subject)) if(FALSE) { ## comment out to avoid R CMD check warning : ## library(glmmADMB) t3 <- system.time(g3 <- glmmadmb(y~Base*trt+Age+Visit+(Visit|subject), data=epil2, family="nbinom")) # t3 : 8.67 sec glmmADMB_epil_vals <- list(fixef= fixef(g3), LL = logLik(g3), theta= g3$alpha) } else { glmmADMB_epil_vals <- list(fixef = c("(Intercept)"= -1.33, "Base"=0.8839167, "trtprogabide"= -0.9299658, "Age"= 0.4751434, "Visit"=-0.2701603, "Base:trtprogabide"=0.3372421), LL = structure(-624.551, class = "logLik", df = 9, nobs = 236L), theta = 7.4702) } if (testLevel > 2) withAutoprint({ ## "too slow" for regular testing -- 49 (MM@lynne: 33, then 26, then 14) seconds: (t4 <- system.time(g4 <- glmer.nb(y ~ Base*trt + Age + Visit + (Visit|subject), data = epil2, verbose=TRUE))) ## 1.1-7 : Warning in checkConv().. failed .. with max|grad| = 0.0089 (tol = 0.001, comp. 4) ## 1.1-21: 2 Warnings: max|grad| = 0.00859, then 0.1176 (0.002, comp. 1) stopifnot(exprs = { all.equal(getNBdisp(g4), glmmADMB_epil_vals$ theta, tolerance= 0.03) # 0.0019777 all.equal(fixef (g4), glmmADMB_epil_vals$ fixef, tolerance= 0.04) # 0.003731 (0.00374 on U 12.04) ## FIXME: even df differ (10 vs 9) ! ## all.equal(logLik.m(g4), - glmmADMB_epil_vals$ LL, tolerance= 0.0) ## was 0.0002 all.equal(logLik.m(g4), # for now {this is not *the* truth, just our current approximation of it}: structure(-624.48418, class = "logLik", df = 10, nobs = 236L), ## tolerance loosened 24-03-2025, failed at 1.7e-4 tolerance = 5e-4) }) }) cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' } ## skip on windows (for speed) lme4/tests/README0000644000176200001440000000125514677066752013164 0ustar liggesusersCatalog of currently-failing examples (commented out, testsx, etc.): glmmExt.R: "fail for MM" on Gaussian/inverse examples -- seems fine for me lmer-0.R: sstudy9 example. Should *not* work; is a meaningful error message possible? prLogistic.R: Thailand/clustered-data example from ?prLogisticDelta example in prLogistic package Presumably the problem is that 100/411 random-effect levels have only zeros -- but should this mess things up? glmmML and lme4.0 give nearly identical answers profile.R: fails on CBPP profiling from testsx: testcolonizer: definite case where complete separation occurs, GLM does not really give a fit testcrabs: ?? not sure ?? lme4/tests/testcolonizer.R0000644000176200001440000000140315022107260015275 0ustar liggesusers## library(lme4.0) ## Emacs M- --> setwd() correctly ## m0.0 <- glm(colonizers~Treatment*homespecies*respspecies, data=randdat, family=poisson) ## with(randdat,tapply(colonizers,list(Treatment,homespecies,respspecies),sum)) ## summary(m1.0 <- glmer(form1, data=randdat, family=poisson)) ## summary(m2.0 <- glmer(form2, data=randdat, family=poisson)) ## detach("package:lme4.0", unload=TRUE) load(system.file("testdata","colonizer_rand.rda",package="lme4")) library("lme4") packageVersion("lme4") if (.Platform$OS.type != "windows") { m1 <- glmer(form1,data=randdat, family=poisson) ## PIRLS step failed m2 <- glmer(form1,data=randdat, family=poisson, nAGQ=0) ## OK m3 <- glmer(form2,data=randdat, family=poisson) ## ditto } ## skip on windows (for speed) lme4/tests/testcrab.R0000644000176200001440000001056215022107260014206 0ustar liggesuserslibrary("lme4") L <- load(system.file("testdata","crabs_randdata2.Rda",package="lme4")) ## randdata0: simulated data, in form suitable for plotting ## randdata: simulated data, in form suitable for analysis ## fr ## alive/dead formula ## fr2 ## proportion alive formula (use with weights=initial.snail.density) ## FIXME: there are still bigger differences than I'd like between the approaches ## (mostly in the random-effects correlation). It's not clear who's right; ## lme4 thinks its parameters are better, but ?? Could be explored further. if (FALSE) { ## library(ggplot2) ## commented to avoid triggering Suggests: requirement library(grid) zmargin <- theme(panel.margin=unit(0,"lines")) theme_set(theme_bw()) g1 <- ggplot(randdata0,aes(x=snail.size,y=surv,colour=snail.size,fill=snail.size))+ geom_hline(yintercept=1,colour="black")+ stat_sum(aes(size=factor(..n..)),alpha=0.6)+ facet_grid(.~ttt)+zmargin+ geom_boxplot(fill=NA,outlier.colour=NULL,outlier.shape=3)+ ## set outliers to same colour as points ## (hard to see which are outliers, but it doesn't really matter in this case) scale_size_discrete("# obs",range=c(2,5)) } if (.Platform$OS.type != "windows") { t1 <- system.time(glmer1 <- glmer(fr2,weights=initial.snail.density, family ="binomial", data=randdata)) t1B <- system.time(glmer1B <- glmer(fr,family ="binomial", data=randdata)) res1 <- c(fixef(glmer1),c(VarCorr(glmer1)$plot)) res1B <- c(fixef(glmer1B),c(VarCorr(glmer1B)$plot)) p1 <- unlist(getME(glmer1,c("theta","beta"))) stopifnot(all.equal(res1,res1B)) dfun <- update(glmer1,devFunOnly=TRUE) stopifnot(all.equal(dfun(p1),c(-2*logLik(glmer1)))) ## ## library(lme4.0) ## version 0.999999.2 results ## t1_lme4.0 <- system.time(glmer1X <- ## glmer(fr2,weights=initial.snail.density, ## family ="binomial", data=randdata)) ## dput(c(fixef(glmer1X),c(VarCorr(glmer1X)$plot))) ## p1X <- c(getME(glmer1X,"theta"),getME(glmer1X,"beta")) p1X <- c(0.681301656652347, -1.14775239687404, 0.436143018123226, 2.77730476938968, 0.609023583738824, -1.60055813739844, 2.0324468778545, 0.624173873057839, -1.7908793509579, -2.44540201631615, -1.42365990002708, -2.26780929006268, 0.700928084600075, -1.26220238391029, 0.369024582097804, 3.44325347343035, 2.26400391093108) stopifnot(all.equal(unname(p1),p1X,tolerance=0.03)) dfun(p1X) dfun(p1) ## ~ 1.8 seconds elapsed time lme4.0_res <- structure(c(2.77730476938968, 0.609023583738824, -1.60055813739844, 2.0324468778545, 0.624173873057839, -1.7908793509579, -2.44540201631615, -1.42365990002708, -2.26780929006268, 0.700928084600075, -1.26220238391029, 0.369024582097804, 3.44325347343035, 2.26400391093108, 0.464171947357232, -0.532754465140956, -0.532754465140956, 0.801690946568518), .Names = c("(Intercept)", "crab.speciesS", "crab.speciesW", "crab.sizeS", "crab.sizeM", "snail.sizeS", "crab.speciesS:crab.sizeS", "crab.speciesS:crab.sizeM", "crab.speciesS:snail.sizeS", "crab.speciesW:snail.sizeS", "crab.sizeS:snail.sizeS", "crab.sizeM:snail.sizeS", "crab.speciesS:crab.sizeS:snail.sizeS", "crab.speciesS:crab.sizeM:snail.sizeS", "", "", "", "")) stopifnot(all.equal(res1,lme4.0_res,tolerance=0.015)) ## library("glmmADMB") ## prop/weights formulation: ~ 7 seconds ## t1_glmmadmb <- system.time(glmer1B <- glmmadmb(fr,family ="binomial", ## corStruct="full",data=randdata)) ## dput(c(fixef(glmer1B),c(VarCorr(glmer1B)$plot))) glmmADMB_res <- structure(c(2.7773101267224, 0.609026276823218, -1.60055704634712, 2.03244174458562, 0.624171008585953, -1.79088398816641, -2.44540300134182, -1.42366043619683, -2.26780858382505, 0.700927141726545, -1.26219964572264, 0.369029052442189, 3.44326297908383, 2.26403738918967, 0.46417, -0.53253, -0.53253, 0.80169), .Names = c("(Intercept)", "crab.speciesS", "crab.speciesW", "crab.sizeS", "crab.sizeM", "snail.sizeS", "crab.speciesS:crab.sizeS", "crab.speciesS:crab.sizeM", "crab.speciesS:snail.sizeS", "crab.speciesW:snail.sizeS", "crab.sizeS:snail.sizeS", "crab.sizeM:snail.sizeS", "crab.speciesS:crab.sizeS:snail.sizeS", "crab.speciesS:crab.sizeM:snail.sizeS", "", "", "", "")) stopifnot(all.equal(res1B,glmmADMB_res,tolerance=0.015)) } ## skip on windows (for speed) lme4/tests/drop.R0000644000176200001440000000127115022107260013340 0ustar liggesusersif (.Platform$OS.type != "windows") withAutoprint({ library(lme4) fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ## slightly weird model but plausible --- not that ## one would want to try drop1() on this model ... fm2 <- lmer(Reaction ~ 1+ (Days|Subject), sleepstudy) drop1(fm2) ## empty update(fm1, . ~ . - Days) anova(fm2) ## empty terms(fm1) terms(fm1,fixed.only=FALSE) extractAIC(fm1) drop1(fm1) drop1(fm1, test="Chisq") gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp, nAGQ=25L) drop1(gm1, test="Chisq") }) ## skip on windows (for speed) lme4/tests/vcov-etc.R0000644000176200001440000001063715022107260014130 0ustar liggesusersstopifnot(require(lme4)) (testLevel <- lme4:::testLevel()) source(system.file("testdata", "lme-tst-funs.R", package="lme4", mustWork=TRUE))# -> unn() ## "MEMSS" is just 'Suggest' -- must still work, when it's missing: if (suppressWarnings(!require(MEMSS, quietly=TRUE)) || (data(ergoStool, package="MEMSS") != "ergoStool")) { cat("'ergoStool' data from package 'MEMSS' is not available --> skipping test\n") } else { fm1 <- lmer (effort ~ Type + (1|Subject), data = ergoStool) ##sp no longer supported since ~ 2012-3: ##sp fm1.s <- lmer (effort ~ Type + (1|Subject), data = ergoStool, sparseX=TRUE) ## was segfaulting with sparseX (a while upto 2010-04-06) fe1 <- fixef(fm1) ##sp fe1.s <- fixef(fm1.s) print(s1.d <- summary(fm1)) ##sp print(s1.s <- summary(fm1.s)) Tse1.d <- c(0.57601226, rep(0.51868384, 3)) stopifnot(exprs = { ##sp all.equal(fe1, fe1.s, tolerance= 1e-12) all.equal(Tse1.d, unname(se1.d <- coef(s1.d)[,"Std. Error"]), tolerance = 1e-6) # std.err.: no too much accuracy is(V.d <- vcov(fm1), "symmetricMatrix") ##sp all.equal(se1.d, coef(s1.s)[,"Std. Error"])#, tol = 1e-10 ##sp all.equal( V.d, vcov(fm1.s))#, tol = 1e-9 all.equal(Matrix::diag(V.d), unn(se1.d)^2, tolerance= 1e-12) }) }## if( ergoStool is available from pkg MEMSS ) ### -------------------------- a "large" example ------------------------- str(InstEval) if (FALSE) { # sparse X is not currently implemented, so forget about this: system.time(## works with 'sparseX'; d has 1128 levels fm7 <- lmer(y ~ d + service + studage + lectage + (1|s), data = InstEval, sparseX=TRUE, verbose=1L, REML=FALSE) ) system.time(sfm7 <- summary(fm7)) fm7 # takes a while as it computes summary() again ! range(t.fm7 <- coef(sfm7)[,"t value"])## -10.94173 10.61535 for REML, -11.03438 10.70103 for ML m.t.7 <- mean(abs(t.fm7), trim = .01) #stopifnot(all.equal(m.t.7, 1.55326395545110, tolerance = 1.e-9)) ##REML value stopifnot(all.equal(m.t.7, 1.56642013605506, tolerance = 1.e-6)) ## ML hist.t <- cut(t.fm7, floor(min(t.fm7)) : ceiling(max(t.fm7))) cbind(table(hist.t)) }# fixed effect 'd' -- with 'sparseX' only -------------------------------- if(testLevel <= 1) { cat('Time elapsed: ', proc.time(),'\n'); q("no") } ## ELSE : (testLevel > 1) : library(lattice) source(system.file("testdata/lme-tst-funs.R", package="lme4", mustWork=TRUE)) ##--> all.equal(), isOptimized(), ... system.time( fm8.N <- lmer(y ~ service * dept + studage + lectage + (1|s) + (1|d), InstEval, REML=FALSE, control=lmerControl("Nelder_Mead"), verbose = 1L) ) ## 14 sec [MM@lynne; 2022-11] ## 62 sec [MM@lynne; 2013-11] ## 59.5 sec [nb-mm3; 2013-12-31] system.time( fm8.B <- lmer(y ~ service * dept + studage + lectage + (1|s) + (1|d), InstEval, REML=FALSE, control=lmerControl("bobyqa"), verbose = 2L) ) ## 7.8 sec [MM@lynne; 2022-11] ## 34.1 sec [nb-mm3; 2013-12-31] system.time( fm8 <- lmer(y ~ service * dept + studage + lectage + (1|s) + (1|d), InstEval, REML=FALSE, verbose = 1L) ) ## 7.8 sec [MM@lynne; 2022-11] stopifnot(isOptimized(fm8.N), isOptimized(fm8.B), isOptimized(fm8)) all.equal(fm8.B, fm8, tolerance=0)# 9.78e-9 (2022-11); both versions of bobyqa all.equal(fm8.B, fm8.N, tolerance=0) ## "Mean relative difference: 3.31 e-06" [nb-mm3; 2013-12-31] stopifnot(isOptimized(fm8.N), isOptimized(fm8.B), isOptimized(fm8)) str(baseOpti(fm8)) str(baseOpti(fm8.N)) str(baseOpti(fm8.B)) (sm8 <- summary(fm8.B)) str(r8 <- ranef(fm8.B)) noquote(sapply(r8, summary)) r.m8 <- cov2cor(vcov(sm8)) Matrix::image(r.m8, main="cor()") if(testLevel <= 2) { cat('Time elapsed: ', proc.time(),'\n'); q("no") } ## ELSE: testLevel > 2 ## Clearly smaller X, but more RE pars ## ==> currently considerably slower than the above system.time( fm9 <- lmer(y ~ studage + lectage + (1|s) + (1|d) + (1|dept:service) + (1|dept), InstEval, verbose = 1L, REML=FALSE) ) ## 25.6 secs [MM@lynne; 2022-11] ## 410 secs [MM@lynne; 2013-11] fm9 (sm9 <- summary(fm9)) rr <- ranef(fm9, condVar = TRUE) ## ~ 6 secs noquote(sapply(rr, summary)) qqr <- qqmath(rr, strip=FALSE) ## NB: x-axis range <==> scale of RE <==> "importance" of effect qqr$d qqr$s dotplot(rr,strip=FALSE)$`dept:service` cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' lme4/tests/prLogistic.R0000644000176200001440000000220514677066752014542 0ustar liggesusers## data set and formula extracted from ?prLogisticDelta example ## (Thailand, clustered-data) in prLogistic package load(system.file("testdata","prLogistic.RData",package="lme4")) library(lme4) (testLevel <- lme4:::testLevel()) if (testLevel > 2) { print(system.time( lme4_est <- glmer(rgi ~ sex + pped + (1|schoolid), data = dataset, family=binomial) )) lme4_results <- list(sigma= sqrt(unname(unlist(VarCorr(lme4_est)))), beta = fixef(lme4_est)) ## stored results from other pkgs glmmML_est <- list(sigma = 1.25365353546143, beta = c("(Intercept)" = -2.19478801858317, "sex" = 0.548884468743364, "pped"= -0.623835613907385)) lme4.0_est <- list(sigma = 1.25369539060849, beta = c("(Intercept)" = -2.19474529099587, "sex" = 0.548900267825802, "pped"= -0.623934772981894)) source(system.file("test-tools-1.R", package = "Matrix"))#-> assert.EQ() etc assert.EQ.(lme4_results, glmmML_est, tol=3e-3) assert.EQ.(lme4_results, lme4.0_est, tol=3e-3) print(lme4_est) } lme4/tests/hatvalues.R0000644000176200001440000000246615022107260014377 0ustar liggesusersif (.Platform$OS.type != "windows") withAutoprint({ library(lme4) source(system.file("testdata", "lme-tst-funs.R", package="lme4", mustWork=TRUE))# -> unn() m <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) bruteForceHat <- function(object) { with(getME(object, c("Lambdat", "Lambda", "Zt", "Z", "q", "X")), { ## cp:= the cross product block matrix in (17) and (18): W <- Diagonal(x = weights(object)) I <- Diagonal(q) A.21 <- t(X) %*% W %*% Z %*% Lambda cp <- rbind(cbind(Lambdat %*% Zt %*% W %*% Z %*% Lambda + I, t(A.21)), cbind(A.21, t(X) %*% W %*% X)) mm <- cbind(Z %*% Lambda, X) ## a bit efficient: both cp and mm are typically quite sparse ## mm %*% solve(as.matrix(cp)) %*% t(mm) mm %*% solve(cp, t(mm), sparse=FALSE) }) } str(H <- bruteForceHat(m)) set.seed(7) ii <- sample(nrow(sleepstudy), 500, replace=TRUE) m2 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy[ii, ]) stopifnot(all.equal(diag(H), unn(hatvalues(m)), tol= 1e-14), all.equal(diag(bruteForceHat(m2)), unn(hatvalues(m2)), tol= 1e-14) ) }) ## skip on windows (for speed) lme4/tests/glmerWarn.R0000644000176200001440000000450515022107260014335 0ustar liggesusersif (.Platform$OS.type != "windows") { library(lme4) library(testthat) ## [glmer(*, gaussian) warns to rather use lmer()] m3 <- suppressWarnings(glmer(Reaction ~ Days + (Days|Subject), sleepstudy)) m4 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) m5 <- suppressWarnings(glmer(Reaction ~ Days + (Days|Subject), sleepstudy, family=gaussian)) expect_equal(fixef(m3),fixef(m5)) ## hack call -- comes out unimportantly different m4@call[[1]] <- quote(lme4::lmer) expect_equal(m3,m4) expect_equal(m3,m5) ## would like m3==m5 != m4 ?? expect_equal(VarCorr(m4), VarCorr(m5), tolerance = 1e-14) print(th4 <- getME(m4,"theta")) expect_equal(th4, getME(m5,"theta"), tolerance = 1e-14) ## glmer() - poly() + interaction if (requireNamespace("mlmRev")) withAutoprint({ data(Contraception, package="mlmRev") ## ch := with child Contraception <- within(Contraception, ch <- livch != "0") ## gmC1 <- glmer(use ~ poly(age,2) + ch + age:ch + urban + (1|district), ## Contraception, binomial) ### not a 'warning' per se {cannot suppressWarnings(.)}: ### fixed-effect model matrix is rank deficient so dropping 1 column / coefficient ### also printed with print(): labeled as "fit warnings" ## ==> from ../R/modular.R chkRank.drop.cols() ## --> Use control = glmerControl(check.rankX = "ignore+drop.cols")) ## because further investigation shows "the problem" is really already ## in model.matrix(): set.seed(101) dd <- data.frame(ch = c("Y","N")[1+rbinom(12, 1, 0.7)], age = rlnorm(12, 16)) colnames(mm1 <- model.matrix( ~ poly(age,2) + ch + age:ch, dd)) ## "(Int.)" "poly(age, 2)1" "poly(age, 2)2" "chY" "chN:age" "chY:age" ## If we make the poly() columns to regular variables, can interact: d2 <- within(dd, { p2 <- poly(age,2); ageL <- p2[,1]; ageQ <- p2[,2]; rm(p2)}) ## then, we can easily get what want (mm2 <- model.matrix( ~ ageL+ageQ + ch + ageL:ch, d2)) ## actually even more compactly now ("drawback": 'ageQ' at end): (mm2. <- model.matrix( ~ ageL*ch + ageQ, d2)) cn2 <- colnames(mm2) stopifnot(identical(mm2[,cn2], mm2.[,cn2])) }) } ## skip on windows (for speed) lme4/tests/optimizer.R0000644000176200001440000000500415022107260014414 0ustar liggesuserslibrary(lme4) source(system.file("test-tools-1.R", package = "Matrix"), keep.source = FALSE) ## N.B. is.all.equal4() and assert.EQ() use 'tol', not 'tolerance' ## should be able to run any example with any bounds-constrained optimizer ... ## Nelder_Mead, bobyqa built in; optimx/nlminb, optimx/L-BFGS-B ## optimx/Rcgmin will require a bit more wrapping/interface work (requires gradient) if (.Platform$OS.type != "windows") { fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ## Nelder_Mead fm1B <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, control=lmerControl(optimizer="bobyqa")) stopifnot(all.equal(fixef(fm1),fixef(fm1B))) require(optimx) lmerCtrl.optx <- function(method, ...) lmerControl(optimizer="optimx", ..., optCtrl=list(method=method)) glmerCtrl.optx <- function(method, ...) glmerControl(optimizer="optimx", ..., optCtrl=list(method=method)) (testLevel <- lme4:::testLevel()) ## FAILS on Windows (on r-forge only, not win-builder)... 'function is infeasible at initial parameters' ## (can we test whether we are on r-forge??) if (.Platform$OS.type != "windows") { fm1C <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, control=lmerCtrl.optx(method="nlminb")) fm1D <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, control=lmerCtrl.optx(method="L-BFGS-B")) stopifnot(is.all.equal4(fixef(fm1),fixef(fm1B),fixef(fm1C),fixef(fm1D))) fm1E <- update(fm1,control=lmerCtrl.optx(method=c("nlminb","L-BFGS-B"))) ## hack equivalence of call and optinfo fm1E@call <- fm1C@call fm1E@optinfo <- fm1C@optinfo assert.EQ(fm1C,fm1E, tol=1e-5, giveRE=TRUE)# prints unless tolerance=0--equality } gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial, control=glmerControl(tolPwrss=1e-13)) gm1B <- update(gm1, control=glmerControl (tolPwrss=1e-13, optimizer="bobyqa")) gm1C <- update(gm1, control=glmerCtrl.optx(tolPwrss=1e-13, method="nlminb")) gm1D <- update(gm1, control=glmerCtrl.optx(tolPwrss=1e-13, method="L-BFGS-B")) stopifnot(is.all.equal4(fixef(gm1),fixef(gm1B),fixef(gm1C),fixef(gm1D), tol=1e-5)) if (testLevel > 1) { gm1E <- update(gm1, control= glmerCtrl.optx(tolPwrss=1e-13, method=c("nlminb","L-BFGS-B"))) ## hack equivalence of call and optinfo gm1E@call <- gm1C@call gm1E@optinfo <- gm1C@optinfo assert.EQ(gm1E,gm1C, tol=1e-5, giveRE=TRUE)# prints unless tol=0--equality } } ## skip on windows (for speed) lme4/tests/varcorr.R0000644000176200001440000000113615022107260014052 0ustar liggesuserslibrary(lme4) if (.Platform$OS.type != "windows") { data(Orthodont, package="nlme") fm1 <- lmer(distance ~ age + (age|Subject), data = Orthodont) VarCorr(fm1) fm2ML <- lmer(diameter ~ 1 + (1|plate) + (1|sample), Penicillin, REML=0) VarCorr(fm2ML) gm1 <- glmer(cbind(incidence,size-incidence) ~ period + (1|herd),data=cbpp, family=binomial) VarCorr(gm1) cbpp$obs <- factor(seq(nrow(cbpp))) gm2 <- update(gm1,.~.+(1|obs)) VarCorr(gm2) if (FALSE) { ## testing lme4/lme4 incompatibility ## library(lme4) VarCorr(fm1) lme4:::VarCorr.merMod(fm1) ## OK } } ## skip on windows (for speed) lme4/tests/confint.R0000644000176200001440000000320115022107260014027 0ustar liggesusersif (lme4:::testLevel() > 1 || .Platform$OS.type != "windows") withAutoprint({ library("lme4") library("testthat") L <- load(system.file("testdata", "lme-tst-fits.rda", package="lme4", mustWork=TRUE)) ## -> "fit_*" objects fm1 <- fit_sleepstudy_2 c0 <- confint(fm1, method="Wald") c0B <- confint(fm1, method="Wald",parm="Days") expect_equal(c0["Days",],c0B["Days",]) expect_equal(c(c0B),c(7.437592,13.496980),tolerance=1e-6) set.seed(101) for (bt in c("norm", "basic", "perc")) { suppressWarnings( confint(fm1, method="boot", boot.type=bt, nsim=10,quiet=TRUE)) } for (bt in c("stud","bca","junk")) { expect_error(confint(fm1, method="boot", boot.type=bt, nsim=10), "should be one of") } if((testLevel <- lme4:::testLevel()) > 1) { pr1.56 <- profile(fm1, which = 5:6) c1 <- confint(pr1.56, method="profile") expect_equal(c0[5:6,],c1,tolerance=2e-3) ## expect Wald and profile _reasonably_ close print(c1,digits=3) ## c6 <- confint(pr1.56, "Days") expect_equal(c1[2, , drop=FALSE], c6) c2 <- confint(fm1,method="boot",nsim=50,parm=5:6) ## expect_error(confint(fm1,method="boot",nsim=50,parm="Days"), ## "must be specified as an integer") expect_equal(c1,c2,tolerance=2e-2) print(c2,digits=3) } if (testLevel > 10) { print(c1B <- confint(fm1, method="profile")) print(c2B <- confint(fm1, method="boot")) expect_equal(unname(c1B), unname(c2B), tolerance=2e-2) } }) ## skip if windows/testLevel<1 lme4/tests/refit.R0000644000176200001440000001546015022107260013512 0ustar liggesusers#### Testing refit() #### ---------------- library(lme4) set.seed(101) testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 ## for each type of model, should be able to ## (1) refit with same data and get the same answer, ## at least structurally (small numerical differences ## are probably unavoidable) ## (2) refit with simulate()d data if (testLevel>1) { getinfo <- function(x) { c(fixef(x), logLik(x), unlist(ranef(x)), unlist(VarCorr(x))) } dropterms <- function(x) { attr(x@frame,"terms") <- NULL x } if (getRversion() >= "3.0.0") { attach(system.file("testdata", "lme-tst-fits.rda", package="lme4")) } else { ## saved fits are not safe with old R versions; just re-compute ("cheat"!): fit_sleepstudy_2 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) cbpp$obs <- factor(seq(nrow(cbpp))) ## intercept-only fixed effect fit_cbpp_0 <- glmer(cbind(incidence, size-incidence) ~ 1 + (1|herd), cbpp, family=binomial) ## include fixed effect of period fit_cbpp_1 <- update(fit_cbpp_0, . ~ . + period) if(FALSE) ## include observation-level RE fit_cbpp_2 <- update(fit_cbpp_1, . ~ . + (1|obs)) ## specify formula by proportion/weights instead fit_cbpp_3 <- update(fit_cbpp_1, incidence/size ~ period + (1 | herd), weights = size) } ## LMM fm1 <- fit_sleepstudy_2 fm1R <- refit(fm1, sleepstudy$Reaction) fm1S <- refit(fm1, simulate(fm1)[[1]]) stopifnot(all.equal(getinfo(fm1 ), getinfo(fm1R), tolerance = 6e-3), all.equal(getinfo(fm1 ), getinfo(fm1S), tolerance = 0.5) # <- simulate() ) if(FALSE) { ## show all differences sapply(slotNames(fm1), function(.) all.equal( slot(fm1,.), slot(fm1R,.), tolerance=0)) } if (getRversion() >= "3.4.0") { ## differences: FALSE for resp, theta, u, devcomp, pp, optinfo? ## FIXME: this isn't actually tested in any way ... sapply(slotNames(fm1), function(.) isTRUE(all.equal( slot(fm1,.), slot(fm1R,.), tolerance= 1.5e-5))) str(fm1 @ optinfo) str(fm1R@ optinfo) } fm1ML <- refitML(fm1) stopifnot( all.equal(getinfo(fm1), getinfo(fm1ML), tolerance=0.05)# 0.029998 ) ## binomial GLMM (two-column) gm1 <- fit_cbpp_1 gm1R <- refit(gm1, with(cbpp, cbind(incidence,size-incidence))) sim1Z <- simulate(gm1)[[1]] sim1Z[4,] <- c(0,0) (gm1. <- refit(gm1, sim1Z)) # earlier gave Error: ... PIRLS ... failed ... all.equal(getinfo(gm1), getinfo(gm1R), tolerance=0) # to see it --> 5.52e-4 # because glmer() uses Laplace approx. (? -- still, have *same* y !) stopifnot(all.equal(getinfo(gm1), getinfo(gm1R), tolerance = 1e-4)) gm1S <- refit(gm1, simulate(gm1)[[1]]) all.equal(getinfo(gm1), getinfo(gm1S), tolerance=0) # to see: stopifnot(all.equal(getinfo(gm1), getinfo(gm1S), tolerance = 0.4)) ## binomial GLMM (prob/weights) formula(gm2 <- fit_cbpp_3) ## glmer(incidence/size ~ period + (1 | herd), cbpp, binomial, weights=size) gm2R <- refit(gm2, with(cbpp, incidence/size)) all.equal(getinfo(gm2), getinfo(gm2R), tolerance= 0) stopifnot(all.equal(getinfo(gm2), getinfo(gm2R), tolerance= 6e-4)) ## FIXME: check on Windows == 2015-06: be brave gm2S <- refit(gm2, simulate(gm2)[[1]]) all.equal(getinfo(gm2), getinfo(gm2S), tolerance=0)# 0.17 .. upto 0.28 stopifnot(all.equal(getinfo(gm2), getinfo(gm2S), tolerance=0.40)) ## from Alexandra Kuznetsova set.seed(101) Y <- matrix(rnorm(1000),ncol=2) d <- data.frame(y1=Y[,1], x=rnorm(100), f=rep(1:10,10)) fit1 <- lmer(y1 ~ x+(1|f),data=d) fit2 <- refit(fit1, newresp = Y[,2], rename.response=TRUE) ## check, but ignore terms attribute of model frame ... tools::assertWarning(refit(fit1, newresp = Y[,2], junk=TRUE)) if (isTRUE(all.equal(fit1,fit2))) stop("fit1 and fit2 should not be equal") ## hack number of function evaluations u2 <- update(fit2) fit2@optinfo$feval <- u2@optinfo$feval <- NA d1 <- dropterms(fit2) d2 <- dropterms( u2 ) ## They are not "all equal", but mostly : for (i in slotNames(d1)) { ae <- all.equal(slot(d1,i), slot(d2,i)) cat(sprintf("%10s: %s\n", i, if(isTRUE(ae)) "all.equal" else paste(ae, collapse="\n "))) } all.equal(getinfo(d1), getinfo(d2), tolerance = 0)# -> 0.00126 stopifnot(all.equal(getinfo(d1), getinfo(d2), tolerance = 0.005)) ## Bernoulli GLMM (specified as factor) if (requireNamespace("mlmRev")) { data(Contraception, package="mlmRev") gm3 <- glmer(use ~ urban + age + livch + (1|district), Contraception, binomial) gm3R <- refit(gm3, Contraception$use) gm3S <- refit(gm3, simulate(gm3)[[1]]) stopifnot(all.equal(getinfo(gm3 ), getinfo(gm3R), tolerance = 1e-5),# 64b_Lx: 7.99e-7 all.equal(getinfo(gm3 ), getinfo(gm3S), tolerance = 0.05) # <- simulated data ) cat("gm3: glmer(..):\n" ); print(getinfo(gm3)) cat("gm3R: refit(*, y):\n" ); print(getinfo(gm3R)) cat("gm3S: refit(*, sim.()):\n"); print(getinfo(gm3S)) data(Mmmec, package="mlmRev") if (lme4:::testLevel() > 1) { gm4 <- glmer(deaths ~ uvb + (1|region), data=Mmmec, family = poisson, offset = log(expected)) ## FIXME: Fails to converge (with larger maxit: "downdate .. not pos.def..") try( gm4R <- refit(gm4, Mmmec $ deaths) ) try( gm4S <- refit(gm4, simulate(gm4)[[1]]) ) if(FALSE) { ## FIXME (above) cat("gm4R: refit(*,y):\n" ); print( getinfo(gm4R) ) cat("gm4S: refit(*,y):\n" ); print( getinfo(gm4S) ) stopifnot(all.equal(getinfo(gm4),getinfo(gm4R),tolerance=6e-5)) } } } ## ---------------------------------------------------------------------- ## issue: #231, http://ms.mcmaster.ca/~bolker/misc/boot_reset.html ## commits: 1a34cd0, e33d698, 53ce966, 7dbfff1, 73aa1bb, a693ba9, 8dc8cf0 ## ---------------------------------------------------------------------- formGrouse <- TICKS ~ YEAR + scale(HEIGHT) + (1 | BROOD) + (1 | INDEX) + (1 | LOCATION) gmGrouse <- glmer(formGrouse, family = "poisson", data = grouseticks) set.seed(105) simTICKS <- simulate(gmGrouse)[[1]] newdata <- transform(grouseticks, TICKS = simTICKS) gmGrouseUpdate <- update(gmGrouse, data = newdata) gmGrouseRefit <- refit(gmGrouse, newresp = simTICKS) ## compute and print tolerances all.equal(bet.U <- fixef(gmGrouseUpdate), bet.R <- fixef(gmGrouseRefit), tolerance = 0) all.equal(th.U <- getME(gmGrouseUpdate, "theta"), th.R <- getME(gmGrouseRefit, "theta"), tolerance = 0) all.equal(dev.U <- deviance(gmGrouseUpdate), dev.R <- deviance(gmGrouseRefit), tolerance = 0) stopifnot( all.equal(bet.U, bet.R, tolerance = 6e-5), # saw 1.0e-5 all.equal( th.U, th.R, tolerance = 4e-5), # saw 1.2e-5 all.equal(dev.U, dev.R, tolerance = 2e-5)) # saw 4.6e-6 } ## testLevel>1 lme4/tests/nlmer.R0000644000176200001440000000473215022107260013516 0ustar liggesuserslibrary(lme4) allEQ <- function(x,y, tolerance = 4e-4, ...) all.equal.numeric(x,y, tolerance=tolerance, ...) (nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ (Asym|Tree), Orange, start = c(Asym = 200, xmid = 725, scal = 350))) fixef(nm1) if (lme4:::testLevel() > 2) { ## 'Theoph' Data modeling Th.start <- c(lKe = -2.5, lKa = 0.5, lCl = -3) system.time(nm2 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKe+lKa+lCl|Subject), Theoph, start = Th.start, control=nlmerControl(tolPwrss=1e-8))) print(nm2, corr=FALSE) system.time(nm3 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKe|Subject) + (lKa|Subject) + (lCl|Subject), Theoph, start = Th.start)) print(nm3, corr=FALSE) ## dropping lKe from random effects: system.time(nm4 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKa+lCl|Subject), Theoph, start = Th.start, control=nlmerControl(tolPwrss=1e-8))) print(nm4, corr=FALSE) system.time(nm5 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKa|Subject) + (lCl|Subject), Theoph, start = Th.start, control=nlmerControl(tolPwrss=1e-8))) print(nm5, corr=FALSE) ## this has not worked in a *long* time anyway, and PKPDmodels is currently archived, so ... ## if (require("PKPDmodels")) { ## oral1cptSdlkalVlCl <- ## PKmod("oral", "sd", list(ka ~ exp(lka), Cl ~ exp(lCl), V ~ exp(lV))) ## if (FALSE) { ## ## FIXME: Error in get(nm, envir = nlenv) : object 'k' not found ## ## probably with environments/call stack etc.? ## ## 'pnames' is c("lV","lka","k") -- not ("lV","lka","lCl") ## ## nlmer -> nlformula -> MkRespMod ## ## pnames are OK in nlformula, but in MkRespMod we try to recover ## ## them from the column names of the gradient attribute of the ## ## model evaluated in nlenv -- which are wrong. ## system.time(nm2a <- nlmer(conc ~ oral1cptSdlkalVlCl(Dose, Time, lV, lka, lCl) ~ ## (lV+lka+lCl|Subject), ## Theoph, start = c(lV=-1, lka=-0.5, lCl=-3), tolPwrss=1e-8)) ## print(nm2a, corr=FALSE) ## } ## } } ## testLevel > 2 lme4/tests/respiratory.R0000644000176200001440000000150615022107260014760 0ustar liggesusers## Data originally from Davis 1991 Stat. Med., as packaged in geepack ## and transformed (center, id -> factor, idctr created, levels labeled) library(lme4) if (.Platform$OS.type != "windows") { load(system.file("testdata","respiratory.RData",package="lme4")) m_glmer_4.L <- glmer(outcome~center+treat+sex+age+baseline+(1|idctr), family=binomial,data=respiratory) m_glmer_4.GHQ5 <- glmer(outcome~center+treat+sex+age+baseline+(1|idctr), family=binomial,data=respiratory,nAGQ=5) m_glmer_4.GHQ8 <- glmer(outcome~center+treat+sex+age+baseline+(1|idctr), family=binomial,data=respiratory,nAGQ=8) m_glmer_4.GHQ16 <- glmer(outcome~center+treat+sex+age+baseline+(1|idctr), family=binomial,data=respiratory,nAGQ=16) } ## skip on windows (for speed) lme4/tests/polytomous.R0000644000176200001440000000247115022107260014631 0ustar liggesuserslibrary(lme4) ## setup ## library(polytomous) ## data(think) ## think.polytomous.lmer1 <- polytomous(Lexeme ~ Agent + Patient + (1|Register), ## data=think, heuristic="poisson.reformulation") ## save("formula.poisson","data.poisson",file="polytomous_test.RData") load(system.file("testdata","polytomous_test.RData",package="lme4")) if (FALSE) { ## infinite loop glmer(formula.poisson,data=data.poisson,family=poisson,verbose=10) ## Cholmod not positive definite -> infinite loop glmer(formula.poisson,data=data.poisson,family=poisson, verbose=10,control=glmerControl(optimizer="bobyqa")) ## caught warning: maxfun < 10 * length(par)^2 is not recommended. -> infinite loop } ## works but sloooow .... if (FALSE) { try(g1 <- glmer(formula.poisson,data=data.poisson,family=poisson, control=glmerControl(compDev=FALSE),verbose=1)) ## runs for 2880 steps until: ## Error in pp$updateDecomp() : Downdated VtV is not positive definite } (testLevel <- lme4:::testLevel()) if (testLevel > 2) { glmer(formula.poisson,data=data.poisson,family=poisson, control=glmerControl(compDev=FALSE,optimizer="bobyqa")) ## caught warning: maxfun < 10 * length(par)^2 is not recommended. ## but runs to completion } lme4/tests/lmList-tst.R0000644000176200001440000000500315022107260014445 0ustar liggesuserslibrary(lme4) options(nwarnings = 1000) if(getRversion() < "3.2.0") { if(interactive()) break # gives an error else q() # <- undesirable when interactive ! } ## Try all "standard" (statistical) S3 methods: .S3generics <- function(class) { s3m <- .S3methods(class=class) ii <- attr(s3m, "info") ii[!ii[, "isS4"], "generic"] } set.seed(12) d <- data.frame( g = sample(c("A","B","C","D","E"), 250, replace=TRUE), y1 = runif(250, max=100), y2 = sample(c(0,1), 250, replace=TRUE) ) fm3.1 <- lmList(y1 ~ 1 | g, data=d) fm3.2 <- lmList(y2 ~ 1 | g, data=d, family=binomial) data(Orthodont, package="nlme") Orthodont <- as.data.frame(Orthodont) # no "groupedData" fm2 <- lmList(distance ~ age | Subject, Orthodont) s3fn <- .S3generics(class= class(fm3.1)[1]) ## works for "old and new" class noquote(s3fn <- s3fn[s3fn != "print"])# <-- it is show() not print() that works ## [1] coef confint fitted fixef formula logLik pairs plot ## [9] predict qqnorm ranef residuals sigma summary update ## In lme4 1.1-7 (July 2014), only these worked: ## coef(), confint(), formula(), logLik(), summary(), update() ## pairs() is excluded for fm3.1 which has only intercept: ## no errors otherwise: evs <- sapply(s3fn[s3fn != "pairs"], do.call, args = list(fm3.1)) cls <- sapply(evs, function(.) class(.)[1]) clsOk <- cls[c("confint", "fixef", "formula", "logLik", "ranef", "sigma", "summary", "update")] stopifnot(identical(unname(clsOk), c("lmList4.confint", "numeric", "formula", "logLik", "ranef.lmList", "numeric", "summary.lmList", "lmList4"))) ## --- fm2 --- non-trivial X: can use pairs(), too: evs2 <- sapply(s3fn, do.call, args = list(fm2)) ## --- fm3.2 --- no failures for this "glmList" : ss <- function(...) suppressMessages(suppressWarnings(...)) ss(evs3.2 <- sapply(s3fn[s3fn != "pairs"], do.call, args = list(fm3.2))) ## --- fm4 --- evs4 <- sapply(s3fn, function(fn) tryCatch(do.call(fn, list(fm4)), error=function(e) e)) length(warnings()) summary(warnings()) ## 4 kinds; glm.fit: fitted probabilities numerically 0 or 1 occurred str(sapply(evs4, class)) # more errors than above isok4 <- !sapply(evs4, is, class2="error") ## includes a nice pairs(): evs4[isok4] ## Error msgs of those with errors, first 5, now 3, then 2 : str(errs4 <- lapply(evs4[!isok4], conditionMessage)) ## $ logLik : chr "log-likelihood not available with NULL fits" ## $ summary: chr "subscript out of bounds" stopifnot(length(errs4) <= 2) lme4/tests/simulate.R0000644000176200001440000001451415022107260014223 0ustar liggesuserslibrary(lme4) library(testthat) (testLevel <- lme4:::testLevel()) L <- load(system.file("testdata/lme-tst-fits.rda", package="lme4", mustWork=TRUE)) if (testLevel>1) { if (getRversion() > "3.0.0") { ## saved fits are not safe with old R versions fm1 <- fit_sleepstudy_1 s1 <- simulate(fm1,seed=101)[[1]] s2 <- simulate(fm1,seed=101,use.u=TRUE) s3 <- simulate(fm1,seed=101,nsim=10) s4 <- simulate(fm1,seed=101,use.u=TRUE,nsim=10) stopifnot(length(s3)==10,all(sapply(s3,length)==180), length(s4)==10,all(sapply(s4,length)==180)) # test hook for cluster random effects fakerand <- function(n) seq(from=1.0, by=0.4, length.out=n) # In fact there are 10 observations/subject for each, but more robustly ns <- tabulate(model.frame(fm1)$Subject) s5 <- simulate(fm1, nsim=1, seed=12345, cluster.rand=fakerand) su <- as.data.frame(VarCorr(fm1))[1, "sdcor"] # sd if cluster effects y1 <- predict(fm1, re.form = ~0, se.fit=FALSE) # fixed effect y2 <- su*rep(fakerand(length(ns)), times = ns) # cluster effect, per individual set.seed(12345) y3 <- sigma(fm1)*rnorm(length(y1)) # individual error terms y <- y1+y2+y3 # testthat::expect_equal automatically incorporates the tolerance *if* # it is in 3rd edition mode. Currently, this package does not use that mode. stopifnot(all(abs(s5-y)< testthat_tolerance())) ## binomial (2-column and prob/weights) gm1 <- fit_cbpp_1 gm2 <- fit_cbpp_3 gm1_s1 <- simulate(gm1,seed=101)[[1]] gm1_s2 <- simulate(gm2,seed=101)[[1]] stopifnot(all.equal(gm1_s1[,1]/rowSums(gm1_s1),gm1_s2)) gm1_s3 <- simulate(gm1,seed=101,use.u=TRUE) gm1_s4 <- simulate(gm1,seed=101,nsim=10) gm1_s5 <- simulate(gm2,seed=101,nsim=10) stopifnot(length(gm1_s4)==10,all(sapply(gm1_s4,ncol)==2),all(sapply(gm1_s4,nrow)==56)) stopifnot(length(gm1_s5)==10,all(sapply(gm1_s5,length)==56)) ## binomial (factor): Kubovy bug report 1 Aug 2013 d <- data.frame(y=factor(rep(letters[1:2],each=100)), f=factor(rep(1:10,10))) g1 <- glmer(y~(1|f),data=d,family=binomial) s6 <- simulate(g1,nsim=10) stopifnot(length(s6)==10,all(sapply(s6,length)==200)) ## test explicitly stated link function gm3 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial(link="logit")) s4 <- simulate(gm3,seed=101)[[1]] stopifnot(all.equal(gm1_s1,s4)) cbpp$obs <- factor(seq(nrow(cbpp))) gm4 <- fit_cbpp_2 ## glmer(cbind(incidence, size - incidence) ~ period + ## (1 | herd) + (1|obs), data = cbpp, family = binomial) s5 <- simulate(gm4,seed=101)[[1]] s6 <- simulate(gm4,seed=101,use.u=TRUE)[[1]] ## Bernoulli ## works, but too slow if (testLevel > 2) { if(require("mlmRev")) { data(guImmun, package="mlmRev") table(guImmun$immun) ## N Y ## 1195 964 g1i <- glmer(immun ~ kid2p+mom25p+ord+ethn+momEd+husEd+momWork+rural+pcInd81+ (1|comm/mom), family="binomial", data=guImmun) ## In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : ## Model failed to converge with max|grad| = 0.326795 (tol = 0.002, component 1) sg1 <- simulate(g1i) if(FALSE) { ## similar: not relevant here {comment out for 'R CMD check'}: ## if(require("glmmTMB")) { g2 <- glmmTMB(immun ~ kid2p+mom25p+ord+ethn+momEd+husEd+momWork+rural+pcInd81+ (1|comm/mom), family="binomial", data=guImmun) sg2 <- simulate(g2) ## } } } } set.seed(101) d <- data.frame(f = factor(rep(LETTERS[1:10],each=10))) d$x <- runif(nrow(d)) u <- rnorm(10) d$eta <- with(d, 1 + 2*x + u[f]) d$y <- rbinom(nrow(d), size=1, prob = plogis(d$eta)) g1 <- glmer(y ~ x + (1|f), data=d, family="binomial") ## tolPwrss=1e-5: no longer necessary if (testLevel > 2) { ## trying a set of glmerControl(tolPwrss = 10^t) : allcoef <- function(x) c(dev = deviance(x), th = getME(x,"theta"), beta = getME(x,"beta")) tfun <- function(t) { gg <- try( ## << errors (too small tolPwrss) are still printed : glmer(y~x+(1|f),data=d,family="binomial", control = glmerControl(tolPwrss = 10^t))) if (inherits(gg,"try-error")) rep(NA,4) else allcoef(gg) } tvec <- seq(-4,-16,by=-0.25) tres <- cbind(t = tvec, t(sapply(tvec, tfun))) print(tres) } gm_s5 <- simulate(g1, seed=102)[[1]] d$y <- factor(c("N","Y")[d$y+1]) g1B <- glmer(y ~ x + (1|f), data=d, family="binomial") ## ,tolPwrss=1e-5) s1B <- simulate(g1B, seed=102)[[1]] stopifnot(all.equal(gm_s5,as.numeric(s1B)-1)) ## another Bernoulli if(requireNamespace("mlmRev")) { data(Contraception, package="mlmRev") gm5 <- glmer(use ~ urban+age+livch+(1|district), Contraception, binomial) s3 <- simulate(gm5) } d$y <- rpois(nrow(d),exp(d$eta)) gm6 <- glmer(y~x+(1|f),data=d,family="poisson") s4 <- simulate(gm6) ## simulation 'from scratch' with formulas: ## binomial ## form <- formula(gm1)[-2] form <- ~ (1|herd) + period gm1_s4 <- simulate(form,newdata=model.frame(gm1), newparams=list(theta=getME(gm1,"theta"), beta=fixef(gm1)), family=binomial, weights=rowSums(model.frame(gm1)[[1]]), seed=101)[[1]] stopifnot(all.equal(gm1_s2,gm1_s4)) gm1_s5 <- simulate(formula(gm1),newdata=cbpp, newparams=list(theta=getME(gm1,"theta"), beta=fixef(gm1)), family=binomial, seed=101)[[1]] stopifnot(all.equal(gm1_s1,gm1_s5)) tt <- getME(gm1,"theta") bb <- fixef(gm1) expect_error(simulate(form,newdata=model.frame(gm1), newparams=list(theta=setNames(tt,"abc"), beta=fixef(gm1)), family=binomial, weights=rowSums(model.frame(gm1)[[1]]), seed=101),"mismatch between") expect_error(simulate(form,newdata=model.frame(gm1), newparams=list(theta=tt, beta=setNames(bb,c("abc",names(bb)[-1]))), family=binomial, weights=rowSums(model.frame(gm1)[[1]]), seed=101),"mismatch between") ## Gaussian form <- formula(fm1)[-2] s7 <- simulate(form,newdata=model.frame(fm1), newparams=list(theta=getME(fm1,"theta"), beta=fixef(fm1), sigma=sigma(fm1)), family=gaussian, seed=101)[[1]] stopifnot(all.equal(s7,s1)) ## TO DO: wider range of tests, including offsets ... }# R >= 3.0.0 } ## testLevel>1 lme4/tests/HSAURtrees.R0000644000176200001440000000456015022107260014325 0ustar liggesusersif (.Platform$OS.type != "windows") withAutoprint({ library("lme4") ## example from HSAUR2 package; data from 'multcomp'; see ../inst/testdata/trees513.R load(system.file("testdata","trees513.RData",package="lme4")) ## model formula: modForm <- damage ~ species - 1 + (1 | lattice / plot) dfun <- glmer(modForm, data = trees513B, family = binomial, devFunOnly = TRUE) ls.str(environment(dfun))# "for your information" .not.call <- function(x) x[names(x) != "call"] if(lme4:::testLevel() < 2) q("no") ## else (testLevel >= 2) : -------------------------------------------------- ## Generate oldres: ## ---------------- ## library(lme4.0) ## system.time(mmod0 <- glmer(damage ~ species - 1 + (1 | lattice / plot), ## data = trees513, family = binomial())) ## ## 4 seconds ## oldres <- c(fixef(mmod0),getME(mmod0,"theta")) ## detach("package:lme4.0") ## dput(oldres) oldres <- structure(c(5.23645064474105, 4.73568475545248, 2.65289926317093, 1.29043984816924, 1.59329381563025, 0.532663142106669, 1.16703186884403 ), .Names = c("speciesspruce", "speciespine", "speciesbeech", "speciesoak", "specieshardwood", "plot:lattice.(Intercept)", "lattice.(Intercept)")) system.time(mmodA <- glmer(modForm, data = trees513A, family = binomial())) ## 7 seconds newres <- c(fixef(mmodA), getME(mmodA,"theta")) stopifnot(all.equal(oldres, newres, tolerance=1.5e-3)) system.time(mmodB <- glmer(modForm, data = trees513B, family = binomial())) ## 10.4 seconds ## if(FALSE) { ## defuncted in 2019-05 [been deprecated since 2013-06] ## lmer( + family) -> diverts to glmer() with a warning [TODO: use assertWarning(.) eventually] system.time(lmodB <- lmer(modForm, data = trees513B, family = binomial())) stopifnot(all.equal(.not.call(summary(mmodB)), .not.call(summary(lmodB)))) newresB <- c(fixef(mmodB),getME(mmodB,"theta")) stopifnot(length(newresB) == length(oldres) + 1)# extra: species[ash/maple/elm/lime] } }) ## skip on windows (for speed) lme4/tests/evalCall.R0000644000176200001440000000073215022107260014120 0ustar liggesusersif (.Platform$OS.type != "windows") { ## see if we can still run lme4 functions when lme4 is not attached if ("package:lme4" %in% search()) detach("package:lme4") data(sleepstudy,package="lme4") data(cbpp,package="lme4") fm1 <- lme4::lmer(Reaction ~ Days + (Days|Subject), sleepstudy) gm1 <- lme4::glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) } ## skip on windows (for speed) lme4/tests/glmerControlPass.R0000644000176200001440000000157215022107260015676 0ustar liggesusersif (.Platform$OS.type != "windows") { ## test redirection from lmer to glmer (correct options passed, ## specifically glmerControl -> tolPwrss library("lme4") library("testthat") ## data("trees513", package = "multcomp") load(system.file("testdata","trees513.RData",package="lme4")) expect_is(mmod1 <- glmer(damage ~ species - 1 + (1 | lattice / plot), data = trees513B, family = binomial()),"glmerMod") if(FALSE) { ## Now (2019-05) defunct; was deprecated since 2013-06: expect_warning(mmod2 <- lmer(damage ~ species - 1 + (1 | lattice / plot), data = trees513B, family = binomial()), "calling lmer with .* is deprecated") mmod2@call <- mmod1@call ## hack calls to equality expect_equal(mmod1,mmod2) } } ## skip on windows (for speed) lme4/tests/boundary.R0000644000176200001440000002103015077667225014240 0ustar liggesusers## In both of these cases boundary fit (i.e. estimate of zero RE ## variance) is *incorrect*. (Nelder_Mead, restart_edge=FALSE) is the ## only case where we get stuck; either optimizer=bobyqa or ## restart_edge=TRUE (default) works if (.Platform$OS.type != "windows") { library(lme4) library(testthat) if(!dev.interactive(orNone=TRUE)) pdf("boundary_plots.pdf") ## Stephane Laurent: dat <- read.csv(system.file("testdata","dat20101314.csv", package="lme4")) fit <- lmer(y ~ (1|Operator)+(1|Part)+(1|Part:Operator), data=dat, control= lmerControl(optimizer="Nelder_Mead")) fit_b <- lmer(y ~ (1|Operator)+(1|Part)+(1|Part:Operator), data=dat, control= lmerControl(optimizer="bobyqa", restart_edge=FALSE)) fit_c <- lmer(y ~ (1|Operator)+(1|Part)+(1|Part:Operator), data=dat, control= lmerControl(optimizer="Nelder_Mead", restart_edge=FALSE, check.conv.hess="ignore")) ## final fit gives degenerate-Hessian warning ## FIXME: use fit_c with expect_warning() as a check on convergence tests ## tolerance=1e-5 seems OK in interactive use but not in R CMD check ... ?? stopifnot(all.equal(getME(fit, "theta") -> th.f, getME(fit_b,"theta"), tolerance=5e-5), all(th.f > 0)) ## Manuel Koller source(system.file("testdata", "koller-data.R", package="lme4")) ldata <- getData(13) ## old (backward compatible/buggy) fm4 <- lmer(y ~ (1|Var2), ldata, control=lmerControl(optimizer="Nelder_Mead", use.last.params=TRUE), start=list(theta=1)) fm4b <- lmer(y ~ (1|Var2), ldata, control = lmerControl(optimizer="Nelder_Mead", use.last.params=TRUE, restart_edge = FALSE, check.conv.hess="ignore", check.conv.grad="ignore"), start = list(theta=1)) ## FIXME: use as convergence test check stopifnot(getME(fm4b,"theta") == 0) fm4c <- lmer(y ~ (1|Var2), ldata, control=lmerControl(optimizer="bobyqa", use.last.params=TRUE), start=list(theta=1)) stopifnot(all.equal(getME(fm4, "theta") -> th4, getME(fm4c,"theta"), tolerance=1e-4), th4 > 0) ## new: doesn't get stuck at edge any more, but gets stuck somewhere else ... fm5 <- lmer(y ~ (1|Var2), ldata, control=lmerControl(optimizer="Nelder_Mead", check.conv.hess="ignore", check.conv.grad="ignore"), start=list(theta=1)) fm5b <- lmer(y ~ (1|Var2), ldata, control=lmerControl(optimizer="Nelder_Mead", restart_edge=FALSE, check.conv.hess="ignore", check.conv.grad="ignore"), start = list(theta = 1)) fm5c <- lmer(y ~ (1|Var2), ldata, control=lmerControl(optimizer="bobyqa"), start = list(theta = 1)) stopifnot(all.equal(unname(getME(fm5c,"theta")), 0.21067645, tolerance = 1e-7)) # 0.21067644264 [64-bit, lynne] if (require("optimx")) { ## additional stuff for diagnosing Nelder-Mead problems. fm5d <- update(fm5,control=lmerControl(optimizer="optimx", optCtrl=list(method="L-BFGS-B"))) fm5e <- update(fm5, control=lmerControl(optimizer="nloptwrap")) mList <- setNames(list(fm4,fm4b,fm4c,fm5,fm5b,fm5c,fm5d,fm5e), c("NM/uselast","NM/uselast/norestart","bobyqa/uselast", "NM","NM/norestart","bobyqa","LBFGSB","nloptr/bobyqa")) pp <- profile(fm5c,which=1) dd <- as.data.frame(pp) par(las=1,bty="l") v <- sapply(mList, function(x) sqrt(VarCorr(x)[[1]])) plot(.zeta^2~.sig01, data=dd, type="b") abline(v=v) res <- cbind(VCorr = sapply(mList, function(x) sqrt(VarCorr(x)[[1]])), theta = sapply(mList, getME,"theta"), loglik = sapply(mList, logLik)) res print(sessionInfo(), locale=FALSE) } ###################### library(lattice) ## testing boundary and near-boundary cases tmpf <- function(i,...) { set.seed(i) d <- data.frame(x=rnorm(60),f=factor(rep(1:6,each=10))) d$y <- simulate(~x+(1|f),family=gaussian,newdata=d, newparams=list(theta=0.01,beta=c(1,1),sigma=5))[[1]] lmer(y~x+(1|f),data=d,...) } sumf <- function(m) { unlist(VarCorr(m))[1] } if (FALSE) { ## figuring out which seeds will give boundary and ## near-boundary solutions mList <- lapply(1:201,tmpf) # [FIXME tons of messages "theta parameters vector not named"] ss <- sapply(mList,sumf)+1e-50 par(las=1,bty="l") hist(log(ss),col="gray",breaks=50) ## values lying on boundary which(log(ss)<(-40)) ## 5, 7-13, 15, 21, ... ## values close to boundary (if check.edge not set) which(log(ss)>(-40) & log(ss) <(-20)) ## 16, 44, 80, 86, 116, ... } ## diagnostic plot tmpplot <- function(i, FUN=tmpf) { dd <- FUN(i, devFunOnly=TRUE) x <- 10^seq(-10,-6.5,length=201) dvec <- sapply(x,dd) op <- par(las=1,bty="l"); on.exit(par(op)) plot(x,dvec-min(dvec)+1e-16, log="xy", type="b") r <- FUN(i) abline(v = getME(r,"theta"), col=2) invisible(r) } ## Case #1: boundary estimate with or without boundary.tol m5 <- tmpf(5) m5B <- tmpf(5,control=lmerControl(boundary.tol=0)) stopifnot(getME(m5, "theta")==0, getME(m5B,"theta")==0) p5 <- profile(m5) ## bobyqa warnings but results look reasonable xyplot(p5) ## reveals slight glitch (bottom row of plots doesn't look right) expect_warning(splom(p5),"unreliable for singular fits") p5B <- profile(m5, signames=FALSE) # -> bobyqa convergence warning (code 3) expect_warning(splom(p5B), "unreliable for singular fits") if(lme4:::testLevel() >= 2) { ## avoid failure to warn ## Case #2: near-boundary estimate, but boundary.tol can't fix it m16 <- tmpplot(16) ## sometimes[2014-11-11] fails (??) : p16 <- profile(m16) ## warning message*s* (non-monotonic profile and more) plotOb <- xyplot(p16) ## NB: It's the print()ing of 'plotOb' which warns ==> need to do this explicitly: expect_warning(print(plotOb), ## warns about linear interpolation in profile for variable 1 "using linear interpolation") d16 <- as.data.frame(p16) xyplot(.zeta ~ .focal|.par, data=d16, type=c("p","l"), scales = list(x=list(relation="free"))) try(splom(p16)) ## breaks when calling predict(.) } ## bottom line: ## * xyplot.thpr could still be improved ## * most of the near-boundary cases are noisy and can't easily be ## fixed tmpf2 <- function(i,...) { set.seed(i) d <- data.frame(x=rnorm(60),f=factor(rep(1:6,each=10)), w=rep(10,60)) d$y <- simulate(~x+(1|f),family=binomial, weights=d$w,newdata=d, newparams=list(theta=0.01,beta=c(1,1)))[[1]] glmer(y~x+(1|f),data=d,family=binomial,weights=w,...) } if (FALSE) { ## figuring out which seeds will give boundary and ## near-boundary solutions mList <- lapply(1:201,tmpf2) ss <- sapply(mList,sumf)+1e-50 par(las=1,bty="l") hist(log(ss),col="gray",breaks=50) ## values lying on boundary head(which(log(ss)<(-50))) ## 1-5, 7 ... ## values close to boundary (if check.edge not set) which(log(ss)>(-50) & log(ss) <(-20)) ## 44, 46, 52, ... } ## m1 <- tmpf2(1) ## FIXME: doesn't work if we generate m1 via tmpf2(1) -- ## some environment lookup problem ... set.seed(1) d <- data.frame(x=rnorm(60),f=factor(rep(1:6,each=10)), w=rep(10,60)) d$y <- simulate(~x+(1|f),family=binomial, weights=d$w,newdata=d, newparams=list(theta=0.01,beta=c(1,1)))[[1]] m1 <- glmer(y~x+(1|f),data=d,family=binomial,weights=w) p1 <- profile(m1) xyplot(p1) expect_warning(splom(p1),"splom is unreliable") } ## skip on windows (for speed) lme4/tests/falsezero_dorie.R0000644000176200001440000000156015022107260015551 0ustar liggesusersif (.Platform$OS.type != "windows") { ## test of false zero problem reported by Vince Dorie ## (no longer occurs with current development lme4) ## https://github.com/lme4/lme4/issues/17 library(lme4) sigma.eps <- 2 sigma.the <- 0.75 mu <- 2 n <- 5 J <- 10 g <- gl(J, n) set.seed(1) theta <- rnorm(J, 0, sigma.eps * sigma.the) y <- rnorm(n * J, mu + theta[g], sigma.eps) lmerFit <- lmer(y ~ 1 + (1 | g), REML = FALSE, verbose=TRUE) y.bar <- mean(y) y.bar.j <- sapply(1:J, function(j) mean(y[g == j])) S.w <- sum((y - y.bar.j[g])^2) S.b <- n * sum((y.bar.j - y.bar)^2) R <- S.b / S.w sigma.the.hat <- sqrt(max((n - 1) * R / n - 1 / n, 0)) stopifnot(all.equal(sigma.the.hat,lme4Sigma <- unname(getME(lmerFit,"theta")), tolerance=2e-5)) } ## skip on windows (for speed) lme4/tests/devCritFun.R0000644000176200001440000000165015022107260014446 0ustar liggesusersif (.Platform$OS.type!="windows") { library(lme4) ## ---------------------------------------------------------------------- ## test that deviance(REMLfit, REML = FALSE) gives the same answer as ## the ML objective function at the REML fit ## ---------------------------------------------------------------------- set.seed(1) w <- runif(nrow(sleepstudy)) fm <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy, weights = w) dfun <- update(fm, devFunOnly = TRUE, REML = FALSE) stopifnot(all.equal(deviance(fm, REML = FALSE), dfun(getME(fm, "theta")))) ## ---------------------------------------------------------------------- ## TODO: test the opposite case that deviance(MLfit, REML = TRUE) ## gives the same answer as the REML objective function at the ML fit ## ---------------------------------------------------------------------- } lme4/tests/glmmExt.R0000644000176200001440000001177115103163201014014 0ustar liggesusers## Tests of a variety of GLMM families and links ## coding: family {g=Gamma, P=Poisson, G=Gaussian, B=binomial} ## link {l=log, i=inverse, c=cloglog, i=identity} ## model {1 = intercept-only, 2 = with continuous predictor} library("lme4") source(system.file("testdata/lme-tst-funs.R", package="lme4", mustWork=TRUE)) ##-> gSim(), a general simulation function ... str(gSim) ## function (nblk = 26, nperblk = 100, sigma = 1, beta = c(4, 3), ## x = runif(n), shape = 2, nbinom = 10, family = Gamma()) if (.Platform$OS.type != "windows") withAutoprint({ set.seed(101) ## Gamma, inverse link (= default) : d <- gSim() ## Gamma, log link eta = log(mu) : dgl <- gSim(dInitial = d, family = Gamma(link = log)) ## Poisson, log link dP <- gSim(dInitial = d, family = poisson()) ## Gaussian, log link --- need to use a non-identity link, otherwise glmer calls lmer dG <- gSim(dInitial = d, family = gaussian(link = log), sd = 2) ## Gaussian with inverse link : (sd small enough to avoid negative values) : dGi <- gSim(dInitial = d, family = gaussian(link = inverse), sd = 0.01) ## binomial with cloglog link dBc <- d dBc$eta <- d$eta - 5 # <==> beta intercept 5 less: otherwise y will be constant dBc <- gSim(dInitial = dBc, ## beta = c(-1, 3), nbinom = 1, family = binomial(link="cloglog")) ## binomial with identity link dBi <- d dBc$eta <- d$eta / 10 # <==> beta slope / 10 : scale so range goes from 0.2-0.8 dBi <- gSim(dInitial = dBc, ## beta = c(4, 3/10), nbinom = 1, family = binomial(link="identity")) ############ ## Gamma/inverse ## GLMs gm0 <- glm(y ~ 1, data=d, family=Gamma) gm1 <- glm(y ~ block-1, data=d, family=Gamma) stopifnot(all.equal(sd(coef(gm1)),1.00753942148611)) gm2 <- glmer(y ~ 1 + (1|block), d, Gamma, nAGQ=0) gm3 <- glmer(y ~ x + (1|block), d, Gamma, nAGQ=0) gm2B <- glmer(y ~ 1 + (1|block), d, Gamma) gm3B <- glmer(y ~ x + (1|block), d, Gamma) ## y ~ x + (1|block), Gamma is TRUE model summary(gm3) summary(gm3B)# should be better ## Both have "correct" beta ~= (4, 3) -- but *too* small (sigma_B, sigma) !! stopifnot(exprs = { all.equal(fixef(gm3 ), c(`(Intercept)` = 4.07253, x = 3.080585), tol = 1e-5) # 1.21e-7 all.equal(fixef(gm3B), c(`(Intercept)` = 4.159398, x = 3.058521),tol = 1e-5) # 1.13e-7 }) VarCorr(gm3) # both variances / std.dev. should be ~ 1 but are too small ## ## library(hglm) ## h1 <- hglm2(y~x+(1|block), data=d, family=Gamma()) ## lme4.0 fails on all of these ... ## Gamma/log ggl1 <- glmer(y ~ 1 + (1|block), data=dgl, family=Gamma(link="log")) ggl2 <- glmer(y ~ x + (1|block), data=dgl, family=Gamma(link="log"))# true model (h.1.2 <- anova(ggl1, ggl2)) stopifnot( all.equal(unlist(h.1.2[2,]), c(npar = 4, AIC = 34216.014, BIC = 34239.467, logLik = -17104.007, "-2*log(L)" = 34208.014, Chisq = 2458.5792, Df = 1, `Pr(>Chisq)` = 0)) ) ## "true" model : summary(ggl2) VarCorr(ggl2) ## ## library(lme4.0) ## ggl1 <- glmer(y ~ 1 + (1|block), data=dgl, family=Gamma(link="log"), verbose= 2) ## fails ## Poisson/log gP1 <- glmer(y ~ 1 + (1|block), data=dP, family=poisson) gP2 <- glmer(y ~ x + (1|block), data=dP, family=poisson) ## Gaussian/log gG1 <- glmer(y ~ 1 + (1|block), data=dG, family=gaussian(link="log")) gG2 <- glmer(y ~ x + (1|block), data=dG, family=gaussian(link="log")) ## works with lme4.0 but AIC/BIC/logLik are crazy, and scale ## parameter is not reported ## glmmML etc. doesn't allow models with scale parameters ## gG1B <- glmmadmb(y ~ 1 + (1|block), data=dG, ## family="gaussian",link="log",verbose=TRUE) ## what is the best guess at the estimate of the scale parameter? ## is it the same as sigma? ## gG1B$alpha ## if(Sys.info()["user"] != "maechler") { # <- seg.faults (MM) ## Gaussian/inverse gGi1 <- glmer(y ~ 1 + (1|block), data=dGi, family=gaussian(link="inverse")) gGi2 <- glmer(y ~ x + (1|block), data=dGi, family=gaussian(link="inverse")) ## Binomial/cloglog gBc1 <- glmer(y ~ 1 + (1|block), data=dBc, family=binomial(link="cloglog")) gBc2 <- glmer(y ~ x + (1|block), data=dBc, family=binomial(link="cloglog")) ## library("glmmADMB") ## glmmadmbfit <- glmmadmb(y ~ x + (1|block), data=dBc, ## family="binomial",link="cloglog") glmmadmbfit <- list(fixef = c("(Intercept)" = -0.717146132730349, x =2.83642900561633), VarCorr = structure(list( block = structure(0.79992, .Dim = c(1L, 1L), .Dimnames = list("(Intercept)", "(Intercept)"))), class = "VarCorr")) stopifnot(all.equal(fixef(gBc2), glmmadmbfit$fixef, tolerance=5e-3)) ## pretty loose tolerance ... stopifnot(all.equal(unname(unlist(VarCorr(gBc2))), c(glmmadmbfit$VarCorr$block), tolerance=2e-2)) gBi1 <- glmer(y ~ 1 + (1|block), data=dBi, family=binomial(link="identity")) gBi2 <- glmer(y ~ x + (1|block), data=dBi, family=binomial(link="identity")) ## FIXME: should test more of the *results* of these efforts, not ## just that they run without crashing ... }) ## skip on windows (for speed) lme4/tests/bootMer.R0000644000176200001440000000636315022107260014012 0ustar liggesusersif (.Platform$OS.type != "windows") { library(lme4) mySumm <- function(.) { s <- sigma(.) c(beta =getME(., "beta"), sigma = s, sig01 = unname(s * getME(., "theta"))) } fm1 <- lmer(Yield ~ 1|Batch, Dyestuff) boo01 <- bootMer(fm1, mySumm, nsim = 10) boo02 <- bootMer(fm1, mySumm, nsim = 10, use.u = TRUE) ## boo02 <- bootMer(fm1, mySumm, nsim = 500, use.u = TRUE) if (require(boot)) { boot.ci(boo02,index=2,type="perc") } fm2 <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake) boo03 <- bootMer(fm2, mySumm, nsim = 10) boo04 <- bootMer(fm2, mySumm, nsim = 10, use.u = TRUE) if (lme4:::testLevel() > 1) { gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) boo05 <- bootMer(gm1, mySumm, nsim = 10) boo06 <- bootMer(gm1, mySumm, nsim = 10, use.u = TRUE) cbpp$obs <- factor(seq(nrow(cbpp))) gm2 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd) + (1|obs), family = binomial, data = cbpp) boo03 <- bootMer(gm2, mySumm, nsim = 10) boo04 <- bootMer(gm2, mySumm, nsim = 10, use.u = TRUE) } load(system.file("testdata","culcita_dat.RData",package="lme4")) cmod <- glmer(predation~ttt+(1|block),family=binomial,data=culcita_dat) set.seed(101) ## FIXME: sensitive to step-halving PIRLS tests ## expect_warning(cc <- confint(cmod,method="boot",nsim=10,quiet=TRUE, ## .progress="txt",PBargs=list(style=3)),"some bootstrap runs failed") library(parallel) if (detectCores()>1) { ## http://stackoverflow.com/questions/12983137/how-do-detect-if-travis-ci-or-not travis <- nchar(Sys.getenv("TRAVIS"))>0 if(.Platform$OS.type != "windows" && !travis) { boo01P <- bootMer(fm1, mySumm, nsim = 10, parallel="multicore", ncpus=2) } ## works in Solaris from an interactive console but not ??? ## via R CMD BATCH if (Sys.info()["sysname"] != "SunOS") boo01P.snow <- bootMer(fm1, mySumm, nsim = 10, parallel="snow", ncpus=2) } set.seed(101) dd <- data.frame(x=runif(200), f=rep(1:20,each=10), o=rnorm(200,mean=2)) dd$y <- suppressMessages(simulate(~x+(1|f)+offset(o), family="poisson", newdata=dd, newparams=list(theta=1,beta=c(0,2)))[[1]]) ## fails under flexLambda dd$y2 <- suppressMessages(simulate(~x+(1|f)+offset(o), family="gaussian", newdata=dd, newparams=list(theta=1,beta=c(0,2),sigma=1))[[1]]) fm3 <- glmer(y~x+(1|f)+offset(o), data=dd,family="poisson") fm4 <- lmer(y2~x+(1|f)+offset(o), data=dd) mySumm2 <- function(fit) return(c(fixef(fit),getME(fit,'theta'))) ## still some issues to fix here bb <- bootMer(fm3,mySumm2,nsim=10) attr(bb,"boot.fail.msgs") bb2 <- bootMer(fm4,mySumm2,nsim=10) } ## skip on windows (for speed) lme4/tests/extras.R0000644000176200001440000000153314677066752013734 0ustar liggesuserslibrary(lme4) ## This example takes long : only for testLevel >= 3 : d.ok <- isTRUE(try(data(star, package = 'mlmRev')) == 'star') if(!interactive() && (lme4:::testLevel() < 3 || !d.ok)) q("no") ## This worked in an *older* version of lme4.0 ## fm1 <- lme4:::carryOver(math ~ gr+sx*eth+cltype+(yrs|id)+(1|tch)+(yrs|sch), ## star, yrs ~ tch/id, ## control = list(msV = 1, nit = 0, grad = 0)) system.time( fm1 <- lmer(math ~ gr + sx*eth + cltype + schtype + hdeg + clad + exp + trace + (yrs | id) + (1 | tch) + (yrs | sch), data = star, verbose = TRUE) ) ## user system elapsed ## 34.991 0.037 35.132 -- lme4.0 ## 36.599 0.031 36.745 -- lme4 {bobyqa; 2014-01-09 @ lynne} sm1 <- summary(fm1) print(sm1, corr=TRUE, symbolic.cor=TRUE)# now message *and* gives the correlation lme4/tests/is.R0000644000176200001440000000160615022107260013011 0ustar liggesusersif (.Platform$OS.type != "windows") { library(lme4) fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) stopifnot(isREML(fm1), isLMM(fm1), !isGLMM(fm1), !isNLMM(fm1)) fm1ML <- refitML(fm1) stopifnot(!isREML(fm1ML), isLMM(fm1ML), !isGLMM(fm1ML), !isNLMM(fm1ML)) gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) stopifnot(!isREML(gm1), !isLMM(gm1), isGLMM(gm1), !isNLMM(gm1)) nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, Orange, start = c(Asym = 200, xmid = 725, scal = 350)) stopifnot(!isREML(nm1), !isLMM(nm1), !isGLMM(nm1), isNLMM(nm1)) } ## skip on windows (for speed) lme4/tests/glmer-1.R0000644000176200001440000002660215022107260013645 0ustar liggesusersif (lme4:::testLevel() > 1 || .Platform$OS.type!="windows") withAutoprint({ ## generalized linear mixed model stopifnot(suppressPackageStartupMessages(require(lme4))) options(show.signif.stars = FALSE) source(system.file("test-tools-1.R", package = "Matrix"), keep.source = FALSE) ## ##' Check that coefficient +- "2" * SD contains true value ##' ##' @title Check that confidence interval for coefficients contains true value ##' @param fm fitted model, e.g., from lm(), lmer(), glmer(), .. ##' @param true.coef numeric vector of true (fixed effect) coefficients ##' @param conf.level confidence level for confidence interval ##' @param sd.factor the "2", i.e. default 1.96 factor for the confidence interval ##' @return TRUE or a string of "error" ##' @author Martin Maechler chkFixed <- function(fm, true.coef, conf.level = 0.95, sd.factor = qnorm((1+conf.level)/2)) { stopifnot(is.matrix(cf <- coefficients(summary(fm))), ncol(cf) >= 2) cc <- cf[,1] sd <- cf[,2] if(any(out1 <- true.coef < cc - sd.factor*sd)) return(sprintf("true coefficient[j], j=%s, is smaller than lower confidence limit", paste(which(out1), collapse=", "))) if(any(out2 <- true.coef > cc + sd.factor*sd)) return(sprintf("true coefficient[j], j=%s, is larger than upper confidence limit", paste(which(out2), collapse=", "))) ## else, return TRUE } ## TODO: (1) move these to ./glmer-ex.R [DONE] ## ---- (2) "rationalize" with ../man/cbpp.Rd #m1e <- glmer1(cbind(incidence, size - incidence) ~ period + (1 | herd), # family = binomial, data = cbpp, doFit = FALSE) ## now #bobyqa(m1e, control = list(iprint = 2L)) m1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp) m1. <- update(m1, start = getME(m1, c("theta", "fixef"))) dm1 <- drop1(m1) stopifnot(all.equal(drop1(m1.), dm1, tol = 1e-10))# Lnx(F28) 64b: 4e-12 ## response as a vector of probabilities and usage of argument "weights" m1p <- glmer(incidence / size ~ period + (1 | herd), weights = size, family = binomial, data = cbpp) ## Confirm that these are equivalent: stopifnot(all.equal(fixef(m1), fixef(m1p)), all.equal(ranef(m1), ranef(m1p)), TRUE) ## for(m in c(m1, m1p)) { ## cat("-------\\n\\nCall: ", ## paste(format(getCall(m)), collapse="\\n"), "\\n") ## print(logLik(m)); cat("AIC:", AIC(m), "\\n") ; cat("BIC:", BIC(m),"\\n") ## } stopifnot(all.equal(logLik(m1), logLik(m1p)), all.equal(AIC(m1), AIC(m1p)), all.equal(BIC(m1), BIC(m1p))) ## changed tolPwrss to 1e-7 to match other default m1b <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp, verbose = 2L, control = glmerControl(optimizer="bobyqa", tolPwrss=1e-7, optCtrl=list(rhobeg=0.2, rhoend=2e-7))) ## using nAGQ=9L provides a better evaluation of the deviance m.9 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp, nAGQ = 9) ## check with nAGQ = 25 m2 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp, nAGQ = 25) ## loosened tolerance on parameters stopifnot(is((cm2 <- coef(m2)), "coef.mer"), dim(cm2$herd) == c(15,4), all.equal(fixef(m2), ### lme4a [from an Ubuntu 11.10 amd64 system] c(-1.39922533406847, -0.991407294757321, -1.12782184600404, -1.57946627431248), ##c(-1.3766013, -1.0058773, ## -1.1430128, -1.5922817), tolerance = 5.e-4, check.attributes=FALSE), all.equal(c(-2*logLik(m2)), 100.010030538022, tolerance=1e-9), all.equal(deviance(m2), 73.373, tolerance=1e-5) ## with bobyqa first (AGQ=0), then ##all.equal(deviance(m2), 101.119749563, tolerance=1e-9) ) ## 32-bit Ubuntu 10.04: coef_m1_lme4.0 <- structure(c(-1.39853505102576, -0.992334712470269, -1.12867541092127, -1.58037389566025), .Names = c("(Intercept)", "period2", "period3", "period4")) ## library(glmmADMB) ## mg <- glmmadmb(cbind(incidence, size - incidence) ~ period + (1 | herd), ## family = "binomial", data = cbpp) coef_m1_glmmadmb <- structure(c(-1.39853810064827, -0.99233330126975, -1.12867317840779, -1.58031150854503), .Names = c("(Intercept)", "period2", "period3", "period4")) ## library(glmmML) ## mm <- glmmML(cbind(incidence, size - incidence) ~ period, ## cluster=herd, ## family = "binomial", data = cbpp) coef_m1_glmmML <- structure(c(-1.39853234657711, -0.992336901732793, -1.12867036466201, -1.58030977686564), .Names = c("(Intercept)", "period2", "period3", "period4")) ## lme4[r 1636], 64-bit ubuntu 11.10: ## c(-1.3788385, -1.0589543, ## -1.1936382, -1.6306271), stopifnot(is((cm1 <- coef(m1b)), "coef.mer"), dim(cm1$herd) == c(15,4), all.equal(fixef(m1b),fixef(m1),tolerance=4e-5), is.all.equal4(fixef(m1b), coef_m1_glmmadmb, coef_m1_lme4.0, coef_m1_glmmML, tol = 5e-4) ) ## Deviance for the new algorithm is lower, eventually we should change the previous test ##stopifnot(deviance(m1) <= deviance(m1e)) showProc.time() # if (require('MASS', quietly = TRUE)) { bacteria$wk2 <- bacteria$week > 2 contrasts(bacteria$trt) <- structure(contr.sdif(3), dimnames = list(NULL, c("diag", "encourage"))) print(fm5 <- glmer(y ~ trt + wk2 + (1|ID), data=bacteria, family=binomial)) showProc.time() # stopifnot( all.equal(logLik(fm5), ## was -96.127838 structure(-96.13069, nobs = 220L, nall = 220L, df = 5L, REML = FALSE, class = "logLik"), tolerance = 5e-4, check.attributes = FALSE) , all.equal(fixef(fm5), ## was 2.834218798 -1.367099481 c("(Intercept)"= 2.831609490, "trtdiag"= -1.366722631, ## now 0.5842291915, -1.599148773 "trtencourage"=0.5840147802, "wk2TRUE"=-1.598591346), tolerance = 1e-4 ) ) } ## Failure to specify a random effects term - used to give an obscure message ## Ensure *NON*-translated message; works on Linux,... : if(.Platform$OS.type == "unix") { Sys.setlocale("LC_MESSAGES", "C") tc <- tryCatch( m2 <- glmer(incidence / size ~ period, weights = size, family = binomial, data = cbpp) , error = function(.) .) stopifnot(inherits(tc, "error"), identical(tc$message, "No random effects terms specified in formula")) } ## glmer - Modeling overdispersion as "mixture" aka ## ----- - *ONE* random effect *PER OBSERVATION" -- example inspired by Ben Bolker: ##' ##' ##'
##' @title ##' @param ng number of groups ##' @param nr number of "runs", i.e., observations per groups ##' @param sd standard deviations of group and "Individual" random effects, ##' (\sigma_f, \sigma_I) ##' @param b true beta (fixed effects) ##' @return a data frame (to be used in glmer()) with columns ##' (x, f, obs, eta0, eta, mu, y), where y ~ Pois(lambda(x)), ##' log(lambda(x_i)) = b_1 + b_2 * x + G_{f(i)} + I_i ##' and G_k ~ N(0, \sigma_f); I_i ~ N(0, \sigma_I) ##' @author Ben Bolker and Martin Maechler rPoisGLMMi <- function(ng, nr, sd=c(f = 1, ind = 0.5), b=c(1,2)) { stopifnot(nr >= 1, ng >= 1, is.numeric(sd), names(sd) %in% c("f","ind"), sd >= 0) ntot <- nr*ng b.reff <- rnorm(ng, sd= sd[["f"]]) b.rind <- rnorm(ntot,sd= sd[["ind"]]) x <- runif(ntot) within(data.frame(x, f = factor(rep(LETTERS[1:ng], each=nr)), obs = 1:ntot, eta0 = cbind(1, x) %*% b), { eta <- eta0 + b.reff[f] + b.rind[obs] mu <- exp(eta) y <- rpois(ntot, lambda=mu) }) } set.seed(1) dd <- rPoisGLMMi(12, 20) m0 <- glmer(y~x + (1|f), family="poisson", data=dd) m1 <- glmer(y~x + (1|f) + (1|obs), family="poisson", data=dd) stopifnot(isTRUE(chkFixed(m0, true.coef = c(1,2))), isTRUE(chkFixed(m1, true.coef = c(1,2)))) (a01 <- anova(m0, m1)) stopifnot(all.equal(a01$Chisq[2], 554.334056, tolerance=1e-5), all.equal(a01$logLik, c(-1073.77193, -796.604902), tolerance=1e-6), a01$ npar == 3:4, na.omit(a01$ Df) == 1) if(lme4:::testLevel() > 1) { nsim <- 10 set.seed(2) system.time( simR <- lapply(1:nsim, function(i) { cat(i,"", if(i %% 20 == 0)"\n") dd <- rPoisGLMMi(10 + rpois(1, lambda=3), 16 + rpois(1, lambda=5)) m0 <- glmer(y~x + (1|f), family="poisson", data=dd) m1 <- glmer(y~x + (1|f) + (1|obs), family="poisson", data=dd) a01 <- anova(m0, m1) stopifnot(a01$ npar == 3:4, na.omit(a01$ Df) == 1) list(chk0 = chkFixed(m0, true.coef = c(1,2)), chk1 = chkFixed(m1, true.coef = c(1,2)), chisq= a01$Chisq[2], lLik = a01$logLik) })) ## m0 is the wrong model, so we don't expect much here: table(unlist(lapply(simR, `[[`, "chk0"))) ## If the fixed effect estimates were unbiased and the standard errors correct, ## and N(0,sigma^2) instead of t_{nu} good enough for the fixed effects, ## the confidence interval should contain the true coef in ~95 out of 100: table(unlist(lapply(simR, `[[`, "chk1"))) ## The tests are all highly significantly in favor of m1 : summary(chi2s <- sapply(simR, `[[`, "chisq")) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 158.9 439.0 611.4 698.2 864.3 2268.0 stopifnot(chi2s > qchisq(0.9999, df = 1)) } showProc.time() }) ## skip if windows and testLevel<1 lme4/tests/methods.R0000644000176200001440000000166515022107260014046 0ustar liggesusersif (.Platform$OS.type != "windows") { library(lme4) library(testthat) fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) expect_equal(colnames(model.frame(fm1)),c("Reaction","Days","Subject")) expect_equal(colnames(model.frame(fm1,fixed.only=TRUE)),c("Reaction","Days")) expect_equal(formula(fm1),Reaction ~ Days + (Days | Subject)) expect_equal(formula(fm1,fixed.only=TRUE),Reaction ~ Days) ## ugly example: model frame with compound elements fm2 <- lmer(log(Reaction) ~ splines::ns(Days,3) + + I(1+Days^3) + (Days|Subject), sleepstudy) expect_equal(names(model.frame(fm2)), c("log(Reaction)", "splines::ns(Days, 3)", "I(1 + Days^3)", "Days", "Subject")) expect_equal(names(model.frame(fm2,fixed.only=TRUE)), c("log(Reaction)", "splines::ns(Days, 3)", "I(1 + Days^3)")) } ## skip on windows (for speed) lme4/tests/lmer-0.R0000644000176200001440000001206415022107260013472 0ustar liggesusersrequire(lme4) source(system.file("test-tools-1.R", package = "Matrix"))# identical3() etc ## use old (<=3.5.2) sample() algorithm if necessary if ("sample.kind" %in% names(formals(RNGkind))) { suppressWarnings(RNGkind("Mersenne-Twister", "Inversion", "Rounding")) } ## Check that quasi families throw an error assertError(lmer(cbind(incidence, size - incidence) ~ period + (1|herd), data = cbpp, family = quasibinomial)) assertError(lmer(incidence ~ period + (1|herd), data = cbpp, family = quasipoisson)) assertError(lmer(incidence ~ period + (1|herd), data = cbpp, family = quasi)) ## check bug found by Kevin Buhr set.seed(7) n <- 10 X <- data.frame(y=runif(n), x=rnorm(n), z=sample(c("A","B"), n, TRUE)) fm <- lmer(log(y) ~ x | z, data=X) ## ignore grouping factors with ## gave error inside model.frame() stopifnot(all.equal(c(`(Intercept)` = -0.834544), fixef(fm), tolerance=.01)) ## is "Nelder_Mead" default optimizer? (isNM <- formals(lmerControl)$optimizer == "Nelder_Mead") (isOldB <- formals(lmerControl)$optimizer == "bobyqa") (isOldTol <- environment(nloptwrap)$defaultControl$xtol_abs == 1e-6) if (.Platform$OS.type != "windows") withAutoprint({ source(system.file("testdata", "lme-tst-funs.R", package="lme4", mustWork=TRUE))# -> uc() ## check working of Matrix methods on vcov(.) etc ---------------------- fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) V <- vcov(fm) (V1 <- vcov(fm1)) (C1 <- chol(V1)) dput(dV <- as.numeric(diag(V))) # 0.17607818634.. [x86_64, F Lnx 36] TOL <- 0 # to show the differences below TOL <- 1e-5 # for the check stopifnot(exprs = { all.equal(dV, uc(if(isNM) 0.176076 else if(isOldB) 0.176068575 else if(isOldTol) 0.1761714 else 0.1760782), tolerance = 9*TOL) # seen 7.8e-8; Apple clang 14.0.3 had 6.3783e-5 all.equal(sqrt(dV), as.numeric(chol(V)), tol = 1e-12) all.equal(diag(V1), uc(`(Intercept)` = 46.5751, Days = 2.38947), tolerance = 40*TOL)# 5e-7 (for "all" algos) is(C1, "dtrMatrix") # was inherits(C1, "Cholesky") dim(C1) == c(2,2) all.equal(as.numeric(C1), # 6.8245967 0. -0.2126263 1.5310962 [x86_64, F Lnx 36] c(6.82377, 0, -0.212575, 1.53127), tolerance=20*TOL)# 1.2e-4 ("all" algos) dim(chol(crossprod(getME(fm1, "Z")))) == 36 }) ## printing signif(chol(crossprod(getME(fm, "Z"))), 5) # -> simple 4 x 4 sparse showProc.time() # ## From: Stephane Laurent ## To: r-sig-mixed-models@.. ## "crash with the latest update of lme4" ## ## .. example for which lmer() crashes with the last update of lme4 ...{R-forge}, ## .. but not with version CRAN version (0.999999-0) lsDat <- data.frame( Operator = as.factor(rep(1:5, c(3,4,8,8,8))), Part = as.factor( c(2L, 3L, 5L, 1L, 1L, 2L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 5L, 1L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 1L, 2L, 2L, 3L, 3L, 4L, 5L, 5L)), y = c(0.34, -1.23, -2.46, -0.84, -1.57,-0.31, -0.18, -0.94, -0.81, 0.77, 0.4, -2.37, -2.78, 1.29, -0.95, -1.58, -2.06, -3.11,-3.2, -0.1, -0.49,-2.02, -0.75, 1.71, -0.85, -1.19, 0.13, 1.35, 1.92, 1.04, 1.08)) xtabs( ~ Operator + Part, data=lsDat) # --> 4 empty cells, quite a few with only one obs.: ## Part ## Operator 1 2 3 4 5 ## 1 0 1 1 0 1 ## 2 2 1 1 0 0 ## 3 2 2 2 1 1 ## 4 1 1 2 2 2 ## 5 1 2 2 1 2 lsD29 <- lsDat[1:29, ] ## FIXME: rank-Z test should probably not happen in this case: (sm3 <- summary(m3 <- lm(y ~ Part*Operator, data=lsDat)))# ok: some interactions not estimable stopifnot(21 == nrow(coef(sm3)))# 21 *are* estimable sm4 <- summary(m4 <- lm(y ~ Part*Operator, data=lsD29)) stopifnot(20 == nrow(coef(sm4)))# 20 *are* estimable lf <- lFormula(y ~ (1|Part) + (1|Operator) + (1|Part:Operator), data = lsDat) dim(Zt <- lf$reTrms$Zt)## 31 x 31 c(rankMatrix(Zt)) ## 21 c(rankMatrix(Zt,method="qr")) ## 31 || 29 (64 bit Lnx), then 21 (!) c(rankMatrix(t(Zt),method="qr")) ## 30, then 21 ! nrow(lsDat) fm3 <- lmer(y ~ (1|Part) + (1|Operator) + (1|Part:Operator), data = lsDat, control=lmerControl(check.nobs.vs.rankZ="warningSmall")) lf29 <- lFormula(y ~ (1|Part) + (1|Operator) + (1|Part:Operator), data = lsD29) (fm4 <- update(fm3, data=lsD29)) fm4. <- update(fm4, REML=FALSE, control=lmerControl(optimizer="nloptwrap", optCtrl=list(ftol_abs=1e-6, xtol_abs=1e-6))) ## summary(fm4.) stopifnot( all.equal(as.numeric(formatVC(VarCorr(fm4.), digits = 7)[,"Std.Dev."]), c(1.040664, 0.6359187, 0.5291422, 0.4824796), tol = 1e-4) ) showProc.time() }) ## skip on windows (for speed) cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' lme4/tests/minval.R0000644000176200001440000000242715022107260013666 0ustar liggesusersif (lme4:::testLevel() > 1 || .Platform$OS.type!="windows") { ## example posted by StĂ©phane Laurent ## exercises bug where Nelder-Mead min objective function value was >0 set.seed(666) sims <- function(I, J, sigmab0, sigmaw0){ Mu <- rnorm(I, mean=0, sd=sigmab0) y <- c(sapply(Mu, function(mu) rnorm(J, mu, sigmaw0))) data.frame(y=y, group=gl(I,J)) } I <- 3 # number of groups J <- 8 # number of repeats per group sigmab0 <- 0.15 # between standard deviation sigmaw0 <- 0.15 # within standard deviation dat <- sims(I, J, sigmab0, sigmaw0) library(lme4) isOldTol <- environment(nloptwrap)$defaultControl$xtol_abs == 1e-6 fm3 <- lmer(y ~ (1|group), data=dat) stopifnot(all.equal(unname(unlist(VarCorr(fm3))), switch(fm3@optinfo$optimizer, "Nelder_Mead" = 0.029662844, "bobyqa" = 0.029662698, "nloptwrap" = if (isOldTol) 0.029679755 else 0.029662699, stop("need new case here: value is ",unname(unlist(VarCorr(fm3)))) ), tolerance = 1e-7)) } ## skip on windows (for speed) lme4/tests/fewlevels.R0000644000176200001440000000132015022107260014363 0ustar liggesusers#### example originally from Gabor Grothendieck source(system.file("testdata/lme-tst-funs.R", package="lme4", mustWork=TRUE)) ##--> rSim.11() testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 if (testLevel>1) { set.seed(1) d1 <- rSim.11(10000, k=4) library(nlme) m.lme <- lme(y ~ x, random=~ 1|fac , data=d1) (VC.lme <- VarCorr(m.lme)) detach("package:nlme") ## library(lme4) fm.NM <- lmer(y ~ x + (1|fac), data=d1, control=lmerControl("Nelder_Mead")) fm.Bq <- update(fm.NM, control=lmerControl("bobyqa")) v.lmer <- VarCorr(fm.NM)[[1]][1,1] stopifnot(all.equal(v.lmer,19.55,tolerance=1e-3)) ## was 19.5482 with old starting values (1), 19.5493 with new start algorithm } ## testLevel>1 lme4/tests/throw.R0000644000176200001440000000157215022107260013543 0ustar liggesusers## original code was designed to detect segfaults/hangs from error handling library(lme4) set.seed(101) d <- expand.grid(block = LETTERS[1:26], rep = 1:100) d$x <- runif(nrow(d)) reff_f <- rnorm(length(levels(d$block)),sd=1) ## need intercept large enough to avoid negative values d$eta0 <- 4+3*d$x ## version without random effects d$eta <- d$eta0+reff_f[d$block] ## inverse link d$mu <- 1/d$eta d$y <- rgamma(nrow(d), scale=d$mu/2, shape=2) if (.Platform$OS.type != "windows") { gm0 <- glmer(y ~ 1|block, d, Gamma) gm0.A25 <- glmer(y ~ 1|block, d, Gamma, nAGQ=25L) gm1 <- glmer(y ~ x + (1|block), d, Gamma) gm1.A25 <- glmer(y ~ x + (1|block), d, Gamma, nAGQ=25L) ## strange things happening for logLik ==> AIC, etc for nAGQ ??? anova(gm0, gm1) anova(gm0, gm0.A25) anova(gm1, gm1.A25) summary(gm1) # "fine" summary(gm1.A25) # Inf logLik etc ? } lme4/tests/resids.R0000644000176200001440000000115114677066752013713 0ustar liggesuserslibrary(lme4) ## raw residuals for LMMs fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) stopifnot(all.equal(residuals(fm1),sleepstudy$Reaction-fitted(fm1))) r1 <- residuals(fm1,type="pearson") ## deviance/Pearson residuals for GLMMs gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp) p <- fitted(gm1) n <- cbpp$size v <- n*p*(1-p) obs_p <- cbpp$incidence/cbpp$size rp <- residuals(gm1,"pearson") rp1 <- (obs_p-p)/sqrt(p*(1-p)) rp2 <- rp1*n ## FIXME:: restore this test ## stopifnot(all.equal(rp,rp2)) r2 <- residuals(gm1,type="deviance") lme4/tests/offset.R0000644000176200001440000000263615022107260013670 0ustar liggesusers## simple examples with offsets, to exercise methods etc. library(lme4) if (.Platform$OS.type != "windows") { ## generate a basic Gamma/random effects sim set.seed(101) d <- expand.grid(block=LETTERS[1:26],rep=1:100) d$x <- runif(nrow(d)) ## sd=1 reff_f <- rnorm(length(levels(d$block)),sd=1) ## need intercept large enough to avoid negative values d$eta0 <- 4+3*d$x ## version without random effects d$eta <- d$eta0+reff_f[d$block] ## lmer() test: d$mu <- d$eta d$y <- rnorm(nrow(d),mean=d$mu,sd=1) fm1 <- lmer(y~x+(1|block), data=d) fm1off <- lmer(y~x+(1|block)+offset(3*x),data=d) ## check equality stopifnot(all.equal(fixef(fm1)[2]-3,fixef(fm1off)[2])) p0 <- predict(fm1) p1 <- predict(fm1,newdata=d) p2 <- predict(fm1off,newdata=d) stopifnot(all.equal(p0,p1), all.equal(p1,p2)) ## glmer() test: d$mu <- exp(d$eta) d$y <- rpois(nrow(d),d$mu) gm1 <- glmer(y~x+(1|block), data=d,family=poisson, control=glmerControl(check.conv.grad="ignore")) gm1off <- glmer(y~x+(1|block)+offset(3*x),data=d,family=poisson, control=glmerControl(check.conv.grad="ignore")) ## check equality stopifnot(all.equal(fixef(gm1)[2]-3,fixef(gm1off)[2],tolerance=3e-4)) p0 <- predict(gm1) p1 <- predict(gm1,newdata=d) p2 <- predict(gm1off,newdata=d) stopifnot(all.equal(p0,p1), all.equal(p1,p2)) ## FIXME: should also test simulations } ## skip on windows (for speed) lme4/tests/ST.R0000644000176200001440000000067614677066752012763 0ustar liggesusersrequire(lme4) # sorry for fitting yet another sleepstudy model in the tests m <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) ST <- getME(m, "ST")$Subject # copied from vince dorie's simmer.R in arm: dimension <- nrow(ST) T <- ST diag(T) <- rep(1, dimension) S <- diag(diag(ST), dimension) vc0 <- getME(m, 'sigma')^2*tcrossprod(T %*% S) vc1 <- VarCorr(m)$Subject[,] dimnames(vc0) <- dimnames(vc1) all.equal(vc0, vc1, tolerance = 1e-6) lme4/tests/predict_basis.R0000644000176200001440000000304115022107260015204 0ustar liggesusers## test for models containing data-defined bases ## ?makepredictcall ## ?model.frame ## ???? data(sleepstudy,package="lme4") library(splines) ## lm0 <- lm(Reaction~ns(Days,2),sleepstudy) ## attr(terms(lm0),"predvars") ## library(nlme) ## lme1 <- lme(Reaction~ns(Days,2),random=~1|Subject,sleepstudy) ## attr(terms(lme1),"predvars") ## no! ## attr(lme1$terms,"predvars") ## yes ## detach("package:nlme") library(lme4) fm1 <- lmer(Reaction ~ ns(Days,2) + (1|Subject), sleepstudy) fm2 <- lmer(Reaction ~ poly(Days,2) + (1|Subject), sleepstudy) fm3 <- lmer(Reaction ~ poly(Days,2,raw=TRUE) + (1|Subject), sleepstudy) newdat0 <- data.frame(Days = unique(sleepstudy$Days)) newdat <- data.frame(Days = 5:12) tmpf <- function(fit) { with(sleepstudy, { plot (Reaction~Days, xlim=c(0,12)) points(Days, predict(fit), col=2) }) lines(newdat0$ Days, predict(fit,re.form=NA,newdata=newdat0), col=4) lines(newdat $ Days, predict(fit,re.form=NA,newdata=newdat ), col=5) } stopifnot(all.equal(predict(fm2,newdat,re.form=NA), predict(fm3,newdat,re.form=NA))) ## pictures tmpf(fm1) tmpf(fm2) tmpf(fm3) ## test for GLMMs set.seed(101) d <- data.frame(y=rbinom(10,size=1,prob=0.5), x=1:10, f=factor(rep(1:5,each=2))) gm1 <- glmer(y ~ poly(x,2) + (1|f), d, family=binomial) gm2 <- glmer(y ~ poly(x,2,raw=TRUE) + (1|f), d, family=binomial) newdat <- data.frame(x=c(1,4,6)) stopifnot(all.equal(predict(gm1,newdat,re.form=NA), predict(gm2,newdat,re.form=NA),tolerance=3e-6)) lme4/tests/testOptControl.R0000644000176200001440000000327215022107260015402 0ustar liggesusers## https://github.com/lme4/lme4/issues/59 library(lme4) dat <- read.csv(system.file("testdata","dat20101314.csv",package="lme4")) NMcopy <- lme4:::Nelder_Mead cc <- capture.output(lmer(y ~ (1|Operator)+(1|Part)+(1|Part:Operator), data=dat, control= lmerControl("NMcopy", optCtrl= list(iprint=20)))) ## check that printing goes through step 140 twice and up to 240 once ## findStep <- function(str,n) sum(grepl(paste0("\\(NM\\) ",n,": "),str)) cc <- paste(cc,collapse="") countStep <- function(str,n) { length(gregexpr(paste0("\\(NM\\) ",n,": "),str)[[1]]) } stopifnot(countStep(cc,140)==2 && countStep(cc,240)==1) ## testStr <- ## "(NM) 20: f = -53.3709 at 0.706667 0.813333 1.46444(NM) 40: f = -147.132 at 0 0 19.18(NM) 60: f = -147.159 at 0 0 17.4275(NM) 80: f = -147.159 at 0 0 17.5615(NM) 100: f = -147.159 at 0 0 17.5754(NM) 120: f = -147.159 at 0 0 17.5769(NM) 140: f = -147.159 at 0 0 17.5768(NM) 20: f = -165.55 at 0.0933333 0.573333 17.3168(NM) 40: f = -173.704 at 0.23799 1.4697 16.9728(NM) 60: f = -173.849 at 0.449634 1.39998 16.9452(NM) 80: f = -174.421 at 0.52329 1.69123 18.1534(NM) 100: f = -176.747 at 0.762043 1.88271 32.8993(NM) 120: f = -176.839 at 0.751206 1.75371 37.2128(NM) 140: f = -176.853 at 0.706425 1.7307 35.7528(NM) 160: f = -176.853 at 0.710803 1.73476 35.7032(NM) 180: f = -176.853 at 0.710159 1.73449 35.6699(NM) 200: f = -176.853 at 0.710271 1.73461 35.6689(NM) 220: f = -176.853 at 0.710259 1.7346 35.6684(NM) 240: f = -176.853 at 0.710257 1.73459 35.6685Linear mixed model fit by REML ['lmerMod']" lme4/tests/getME.R0000644000176200001440000000430415022107260013375 0ustar liggesusersif (.Platform$OS.type != "windows") { library(lme4) #### tests of getME() ### are names correct? -------------- if(getRversion() < "2.15") paste0 <- function(...) paste(..., sep = '') hasInms <- function(x) grepl("(Intercept", names(x), fixed=TRUE) matchNms <- function(fm, PAR) { stopifnot(is.character(vnms <- names(fm@cnms))) mapply(grepl, paste0("^", vnms), names(PAR)) } chkIMod <- function(fm) {## check "intercept only" model b1 <- getME(fm,"beta") f1 <- fixef(fm) stopifnot(hasInms(f1), f1 == b1, hasInms(t1 <- getME(fm,"theta")), matchNms(fm, t1)) } fm1 <- lmer(diameter ~ (1|plate) + (1|sample), Penicillin) chkIMod(fm1) fm2 <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake) stopifnot(fixef(fm2) == getME(fm2,"beta")) getME(fm2,"theta") getME(fm3 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy), "theta") getME(fm4 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy), "theta") ## internal consistency check ensuring that all allowed 'name's work (and are not empty): (nmME <- eval(formals(lme4:::getME.merMod)$name)) chkMEs <- function(fm, nms) { stopifnot(is.character(nms)) str(parts <- sapply(nms, getME, object = fm, simplify=FALSE)) isN <- sapply(parts, is.null) stopifnot(identical(names(isN), nms), !any(isN)) } chkMEs(fm1, nmME) chkMEs(fm2, nmME) chkMEs(fm3, nmME) chkMEs(fm4, nmME) ## multiple components can now be retrieved at once gg <- getME(fm2,c("theta","beta")) gg2 <- getME(fm2,c("theta","beta","X")) ## list of Zt for each random-effects factor lapply(getME(fm2,c("Ztlist")),dim) ## Cholesky factors returned as a list of matrices getME(fm1,"ST") getME(fm2,"ST") ## distinction between number of RE terms ## and number of RE grouping factors stopifnot(getME(fm2,"n_rtrms")==1) stopifnot(getME(fm2,"n_rfacs")==1) lapply(getME(fm4,c("Ztlist")),dim) stopifnot(getME(fm4,"n_rtrms")==2) stopifnot(getME(fm4,"n_rfacs")==1) stopifnot(getME(fm1,"sigma")==sigma(fm1)) } ## skip on windows (for speed) lme4/tests/test-glmernbref.R0000644000176200001440000000047615022107260015502 0ustar liggesusers## DON'T load lme4; test is to see if glmer.nb works when ## lme4 is not loaded set.seed(101) dd <- data.frame(x=runif(200), f= rep(1:20, each=10)) b <- rnorm(20) dd <- transform(dd, y = rnbinom(200, mu = exp(1 + 2*x + b[f]), size = 2)) g <- lme4::glmer.nb(y~x + (1|f), data = dd) stopifnot(inherits(g, "glmerMod")) lme4/tests/lmer-1.R0000644000176200001440000003144615077667225013526 0ustar liggesusers### suppressPackageStartupMessages(...) as we have an *.Rout.save to Rdiff against stopifnot(suppressPackageStartupMessages(require(lme4))) options(show.signif.stars = FALSE, useFancyQuotes=FALSE) source(system.file("test-tools-1.R", package = "Matrix"))# identical3() etc all.EQ <- function(u,v, ...) all.equal.X(u, v, except = c("call", "frame"), ...) S4_2list <- function(obj) { # no longer used sn <- slotNames(obj) structure(lapply(sn, slot, object = obj), .Names = sn) } if (lme4:::testLevel() <= 1) quit("no") ## otherwise *print* normally: oldOpts <- options(digits=2) (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (fm1a <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, REML = FALSE)) (fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) anova(fm1, fm2) ## Now works for glmer fm1. <- suppressWarnings(glmer(Reaction ~ Days + (Days|Subject), sleepstudy)) ## default family=gaussian/identity link -> automatically calls lmer() (but with a warning) ## hack call -- comes out unimportantly different fm1.@call[[1]] <- quote(lmer) stopifnot(all.equal(fm1, fm1.)) ## Test against previous version in lmer1 (using bobyqa for consistency) #(fm1. <- lmer1(Reaction ~ Days + (Days|Subject), sleepstudy, opt = "bobyqa")) #stopifnot(all.equal(fm1@devcomp$cmp['REML'], fm1.@devcomp$cmp['REML']), # all.equal(fixef(fm1), fixef(fm1.)), # all.equal(fm1@re@theta, fm1.@theta, tolerance = 1.e-7), # all.equal(ranef(fm1), ranef(fm1.))) ## compDev = FALSE no longer applies to lmer ## Test 'compDev = FALSE' (vs TRUE) ## fm1. <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, ## compDev = FALSE)#--> use R code (not C++) for deviance computation ## stopifnot(all.equal(fm1@devcomp$cmp['REML'], fm1.@devcomp$cmp['REML']), ## all.equal(fixef(fm1), fixef(fm1.)), ## all.equal(fm1@re@theta, fm1.@re@theta, tolerance = 1.e-7), ## all.equal(ranef(fm1), ranef(fm1.), tolerance = 1.e-7)) vv <- vcov(fm1) cc <- Matrix::cov2cor(vv) dimnames(cc) <- dimnames(vv) ## work around Matrix 1.5.2 buglet stopifnot( all.equal(fixef(fm1), fixef(fm2), tolerance = 1.e-13) , all.equal(unname(fixef(fm1)), c(251.405104848485, 10.467285959595), tolerance = 1e-13) , all.equal(cc["(Intercept)", "Days"], -0.1375, tolerance = 4e-4) ) fm1ML <- refitML(fm1) fm2ML <- refitML(fm2) (cbind(AIC= c(m1= AIC(fm1ML), m2= AIC(fm2ML)), BIC= c( BIC(fm1ML), BIC(fm2ML))) -> ICm) stopifnot(all.equal(c(ICm), c(1763.94, 1762, 1783.1, 1777.97), tolerance = 1e-5))# see 1.2e-6 (fm3 <- lmer(Yield ~ 1|Batch, Dyestuff2)) stopifnot(all.equal(coef(summary(fm3)), array(c(5.6656, 0.67838803150, 8.3515624346), c(1,3), dimnames = list("(Intercept)", c("Estimate", "Std. Error", "t value"))))) showProc.time() # ### {from ../man/lmer.Rd } --- compare lmer & lmer1 --------------- (fmX1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (fm.1 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) #(fmX2 <- lmer2(Reaction ~ Days + (Days|Subject), sleepstudy)) #(fm.2 <- lmer2(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) ## check update(, ): fm.3 <- update(fmX1, . ~ Days + (1|Subject) + (0+Days|Subject)) stopifnot(all.equal(fm.1, fm.3)) fmX1s <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy )# no longer:, sparseX=TRUE) #fmX2s <- lmer2(Reaction ~ Days + (Days|Subject), sleepstudy, sparseX=TRUE) options(oldOpts) ## restore digits showProc.time() # for(nm in c("coef", "fixef", "ranef", "sigma", "model.matrix", "model.frame" , "terms")) { cat(sprintf("%15s : ", nm)) FUN <- get(nm) F.fmX1s <- FUN(fmX1s) # F.fmX2s <- FUN(fmX2s) # if(nm == "model.matrix") { # F.fmX1s <- as(F.fmX1s, "denseMatrix") # F.fmX2s <- as(F.fmX2s, "denseMatrix") # FF <- function(.) {r <- FUN(.); row.names(r) <- NULL # as(r, "generalMatrix") } # } # else FF <- FUN stopifnot( all.equal( FF(fmX1), F.fmX1s, tolerance = 1e-6) # , # all.equal( FF(fmX2), F.fmX2s, tolerance = 1e-5) # , # all.equal( FF(fm.1), F.fmX2s, tolerance = 9e-6) ## these are different models # , # all.equal(F.fmX2s, F.fmX1s, tolerance = 6e-6) # , # all.equal(FUN(fm.1), FUN(fm.2), tolerance = 6e-6) , TRUE) cat("[Ok]\n") } ## transformed vars should work[even if non-sensical as here;failed in 0.995-1] fm2l <- lmer(log(Reaction) ~ log(Days+1) + (log(Days+1)|Subject), data = sleepstudy, REML = FALSE) ## no need for an expand method now : xfm2 <- expand(fm2) stopifnot(dim(ranef(fm2l)[[1]]) == c(18, 2), is((c3 <- coef(fm3)), "coef.mer"), all(fixef(fm3) == c3$Batch),## <-- IFF \hat{\sigma^2} == 0 TRUE) ## Simple example by Andrew Gelman (2006-01-10) ---- n.groups <- 10 ; n.reps <- 2 n <- length(group.id <- gl(n.groups, n.reps)) ## simulate the varying parameters and the data: set.seed(0) a.group <- rnorm(n.groups, 1, 2) y <- rnorm (n, a.group[group.id], 1) ## fit and summarize the model fit.1 <- lmer (y ~ 1 + (1 | group.id)) oldOpts <- options(digits=3) coef (fit.1) options(oldOpts) ## check show( <"summary.mer"> ): (sf1 <- summary(fit.1)) # --> now looks as for fit.1 stopifnot(all.equal(fixef(fit.1), c("(Intercept)" = 1.571312129)), all.equal(unname(ranef(fit.1, drop=TRUE)[["group.id"]]), structure( c(1.8046888, -1.8097665, 1.6146451, 1.5408268, -0.1331995, -3.3306655, -1.8259277, -0.8735145, -0.3591311, 3.3720441), postVar = rep.int(0.311091076, 10)), tolerance = 1e-5) ) ## ranef and coef rr <- ranef(fm1) stopifnot(is.list(rr), length(rr) == 1, is.data.frame(rr[[1]])) print(plot(rr)) stopifnot(is(cc <- coef(fm1), "coef.mer"), is.list(cc), length(cc) == 1, is.data.frame(cc[[1]])) print(plot(cc)) rr <- ranef(fm2) stopifnot(is.list(rr), length(rr) == 1, is.data.frame(rr[[1]])) print(plot(rr)) stopifnot(is(cc <- coef(fm2), "coef.mer"), is.list(cc), length(cc) == 1, is.data.frame(cc[[1]])) print(plot(cc)) showProc.time() # ## Invalid factor specification -- used to seg.fault: set.seed(1) dat <- within(data.frame(lagoon = factor(rep(1:4,each = 25)), habitat = factor(rep(1:20, each = 5))), { y <- round(10*rnorm(100, m = 10*as.numeric(lagoon))) }) tt <- suppressWarnings(try(reg <- lmer(y ~ habitat + (1|habitat*lagoon), data = dat) ) # did seg.fault) ) # now gives error ^- should be ":" ## suppress warning that uses different quoting conventions on ## R-release vs. R-devel ## ignore singular fits as well as hess/grad problems ## (Windows gets singular fits, other platforms don't ...) ctrl0 <- lmerControl( check.conv.singular="ignore", check.conv.hess="ignore", check.conv.grad="ignore") r1 <- lmer(y ~ 0+habitat + (1|habitat:lagoon), data = dat, control=ctrl0) # ok, but senseless r1b <- lmer(y ~ 0+habitat + (1|habitat), data = dat, control=ctrl0) # same model, clearly unidentifiable ## "TODO" : summary(r1) should ideally warn the user stopifnot(all.equal(fixef(r1), fixef(r1b), tolerance= 1e-15), all.equal(ranef(r1), ranef(r1b), tolerance= 1e-15, check.attributes=FALSE)) ## Use a more sensible model: r2.0 <- lmer(y ~ 0+lagoon + (1|habitat:lagoon), data = dat) # ok r2 <- lmer(y ~ 0+lagoon + (1|habitat), data = dat) # ok, and more clear stopifnot(all.equal(fixef(r2), fixef(r2.0), tolerance= 1e-15), all.equal(ranef(r2), ranef(r2.0), tolerance= 1e-15, check.attributes=FALSE)) V2 <- vcov(r2) assert.EQ.mat(V2, diag(x = 9.9833/3, nr = 4)) stopifnot(all.equal(unname(fixef(r2)) - (1:4)*100, c(1.72, 0.28, 1.76, 0.8), tolerance = 1e-13)) ## sparseX version should give same numbers: ## (only gives a warning now -- sparseX disregarded) if(FALSE) { ## no longer r2. <- lmer(y ~ 0+lagoon + (1|habitat), data = dat, sparseX = TRUE) ## the summary() components we do want to compare 'dense X' vs 'sparse X': nmsSumm <- c("methTitle", "devcomp", "logLik", "ngrps", "coefficients", "sigma", "REmat", "AICtab") sr2 <- summary(r2) sr2. <- summary(r2.) sr2.$devcomp$dims['spFe'] <- 0L # to allow for comparisons below stopifnot(all.equal(sr2[nmsSumm], sr2.[nmsSumm], tolerance= 1e-14) , all.equal(ranef(r2), ranef(r2.), tolerance= 1e-14) , Matrix:::isDiagonal(vcov(r2.)) # ok , all.equal(Matrix::diag(vcov(r2.)), rep.int(V2[1,1], 4), tolerance= 1e-13) # , all(vcov(r2.)@factors$correlation == diag(4)) # not sure why this fails , TRUE) r2. } ### mcmcsamp() : ## From: Andrew Gelman ## Date: Wed, 18 Jan 2006 22:00:53 -0500 if (FALSE) { # mcmcsamp still needs work ## NB: Need to restore coda to the Suggests: field of DESCRIPTION ## file if this code block is reinstated. ## has.coda <- require(coda) ## if(!has.coda) ## cat("'coda' package not available; some outputs will look suboptimal\n") ## Very simple example y <- 1:10 group <- gl(2,5) (M1 <- lmer (y ~ 1 + (1 | group))) # works fine (r1 <- mcmcsamp (M1)) # dito r2 <- mcmcsamp (M1, saveb = TRUE) # gave error in 0.99-* and 0.995-[12] (r10 <- mcmcsamp (M1, n = 10, saveb = TRUE)) ## another one, still simple y <- (1:20)*pi x <- (1:20)^2 group <- gl(2,10) M1 <- lmer (y ~ 1 | group) mcmcsamp (M1, n = 2, saveb=TRUE) # fine M2 <- lmer (y ~ 1 + x + (1 + x | group)) # false convergence ## should be identical (and is) M2 <- lmer (y ~ x + ( x | group))# false convergence -> simulation doesn't work: if(FALSE) ## try(..) fails here (in R CMD check) [[why ??]] mcmcsamp (M2, saveb=TRUE) ## Error: inconsistent degrees of freedom and dimension ... ## mcmc for glmer: rG1k <- mcmcsamp(m1, n = 1000) summary(rG1k) rG2 <- mcmcsamp(m1, n = 3, verbose = TRUE) } ## Spencer Graves' example (from a post to S-news, 2006-08-03) ---------------- ## it should give an error, rather than silent non-sense: tstDF <- data.frame(group = letters[1:5], y = 1:5) assertError(## Now throws an error, as desired : lmer(y ~ 1 + (1|group), data = tstDF) ) showProc.time() # ## Wrong formula gave a seg.fault at times: set.seed(2)# ! D <- data.frame(y= rnorm(12,10), ff = gl(3,2,12), x1=round(rnorm(12,3),1), x2=round(rnorm(12,7),1)) ## NB: The first two are the same, having a length-3 R.E. with 3 x 3 vcov-matrix: ## --> do need CPU ## suppressWarnings() for warning about too-few random effects levels tmpf <- function(form) lmer(form, data = D , control=lmerControl(check.conv.singular="ignore", check.nobs.vs.nRE="ignore", calc.derivs=FALSE)) m0 <- tmpf(y ~ (x1 + x2)|ff) m1 <- tmpf(y ~ x1 + x2|ff) m2 <- tmpf(y ~ x1 + (x2|ff)) m3 <- tmpf(y ~ (x2|ff) + x1) suppressWarnings(stopifnot(all.equal(ranef(m0), ranef(m1), tolerance = 1e-5), all.equal(ranef(m2), ranef(m3), tolerance = 1e-5), inherits(tryCatch(lmer(y ~ x2|ff + x1, data = D), error = function(e)e), "error"))) showProc.time() # ## Reordering of grouping factors should not change the internal structure #Pm1 <- lmer1(strength ~ (1|batch) + (1|sample), Pastes, doFit = FALSE) #Pm2 <- lmer1(strength ~ (1|sample) + (1|batch), Pastes, doFit = FALSE) #P2.1 <- lmer (strength ~ (1|batch) + (1|sample), Pastes, devFunOnly = TRUE) #P2.2 <- lmer (strength ~ (1|sample) + (1|batch), Pastes, devFunOnly = TRUE) ## The environments of Pm1 and Pm2 should be identical except for ## "call" and "frame": #stopifnot(## all.EQ(env(Pm1), env(Pm2)), # all.EQ(S4_2list(P2.1), # S4_2list(P2.2))) ## example from Kevin Thorpe: synthesized equivalent ## http://thread.gmane.org/gmane.comp.lang.r.lme4.devel/9835 ## NA issue: simpler example d <- data.frame(y=1:60,f=factor(rep(1:6,each=10))) d$y[2] <- NA d$f[3:4] <- NA lmer(y~(1|f),data=d) glmer(y~(1|f),data=d,family=poisson) ## we originally thought that these examples should be ## estimating non-zero variances, but they shouldn't ... ## number of levels with each level of replication levs <- c(800,300,150,100,50,50,50,20,20,5,2,2,2,2) n <- seq_along(levs) flevels <- seq(sum(levs)) set.seed(101) fakedat <- data.frame(DA = factor(rep(flevels,rep(n,levs))), zbmi=rnorm(sum(n*levs))) ## add NA values fakedat[sample(nrow(fakedat),100),"zbmi"] <- NA fakedat[sample(nrow(fakedat),100),"DA"] <- NA m5 <- lmer(zbmi ~ (1|DA) , data = fakedat, control=lmerControl(check.nobs.vs.rankZ="ignore")) m6 <- update(m5, data=na.omit(fakedat)) stopifnot(VarCorr(m5)[["DA"]] == 0, VarCorr(m6)[["DA"]] == 0) showProc.time() lme4/tests/profile-tst.R0000644000176200001440000001353415022107260014651 0ustar liggesuserslibrary(lme4) library(testthat) library(lattice) testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 options(nwarnings = 5000)# instead of 50, and then use summary(warnings()) if (testLevel>1) { ### __ was ./profile_plots.R ___ fm1 <- lmer(Reaction~ Days + (Days|Subject), sleepstudy) pfile <- system.file("testdata","tprfm1.RData", package="lme4") if(file.exists(pfile)) print(load(pfile)) else withAutoprint({ system.time( tpr.fm1 <- profile(fm1, optimizer="Nelder_Mead") ) ## 5 sec (2018); >= 50 warnings !? save(tpr.fm1, file= "../../inst/testdata/tprfm1.RData") }) oo <- options(warn = 2) # {warnings are errors from here on} if(!dev.interactive(orNone=TRUE)) pdf("profile_plots.pdf") xyplot(tpr.fm1) splom(tpr.fm1) densityplot(tpr.fm1, main="densityplot( profile(lmer(..)) )") ## various scale options xyplot(tpr.fm1,scale=list(x=list(relation="same"))) ## stupid xyplot(tpr.fm1,scale=list(y=list(relation="same"))) xyplot(tpr.fm1,scale=list(y=list(relation="same"),tck=0)) ## expect_error(xyplot(tpr.fm1,conf=50),"must be strictly between 0 and 1") ### end {profile_plots.R} fm01ML <- lmer(Yield ~ 1|Batch, Dyestuff, REML = FALSE) ## 0.8s (on a 5600 MIPS 64bit fast(year 2009) desktop "AMD Phenom(tm) II X4 925"): ## system.time( tpr <- profile(fm01ML) ) ## test all combinations of 'which', including plots (but don't show plots) wlist <- list(1:3,1:2,1,2:3,2,3,c(1,3)) invisible(lapply(wlist,function(w) xyplot(profile(fm01ML,which=w)))) (confint(tpr) -> CIpr) print(xyplot(tpr)) ## comparing against lme4a reference values -- but lme4 returns sigma ## rather than log(sigma) stopifnot(dim(CIpr) == c(3,2), all.equal(unname(CIpr[".sigma",]),exp(c(3.64362, 4.21446)), tolerance=1e-6), all.equal(unname(CIpr["(Intercept)",]),c(1486.451500,1568.548494))) options(oo)# warnings allowed .. ## fixed-effect profiling with vector RE data(Pastes) fmoB <- lmer(strength ~ 1 + (cask | batch), data=Pastes, control = lmerControl(optimizer = "bobyqa")) (pfmoB <- profile(fmoB, which = "beta_", alphamax=.001)) xyplot(pfmoB)# nice and easy .. summary( fm <- lmer(strength ~ 1 + (cask | batch), data=Pastes, control = lmerControl(optimizer = "nloptwrap", calc.derivs= FALSE)) ) ls.str(environment(nloptwrap))# showing *its* defaults pfm <- profile(fm, which = "beta_", alphamax=.001) # 197 warnings for "nloptwrap" summary(warnings()) str(pfm) # only 3 rows, .zeta = c(0, NaN, Inf) !!! try( xyplot(pfm) ) ## FIXME or rather the profiling or rather the "wrap on nloptr" (testLevel <- lme4:::testLevel()) if(testLevel > 2) { ## 2D profiles fm2ML <- lmer(diameter ~ 1 + (1|plate) + (1|sample), Penicillin, REML=0) system.time(pr2 <- profile(fm2ML)) # 5.2 sec, 2018-05: 2.1" (confint(pr2) -> CIpr2) lme4a_CIpr2 <- structure(c(0.633565787613112, 1.09578224011285, -0.721864513060904, 21.2666273835452, 1.1821039843372, 3.55631937954106, -0.462903300019305, 24.6778176174587), .Dim = c(4L, 2L), .Dimnames = list(c(".sig01", ".sig02", ".lsig", "(Intercept)"), c("2.5 %", "97.5 %"))) lme4a_CIpr2[".lsig",] <- exp(lme4a_CIpr2[".lsig",]) stopifnot(all.equal(unname(CIpr2),unname(lme4a_CIpr2),tolerance=1e-6)) print(xyplot(pr2, absVal=0, aspect=1.3, layout=c(4,1))) print(splom(pr2)) gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) ## GLMM profiles system.time(pr4 <- profile(gm1)) ## ~ 10 seconds pr4.3 <- profile(gm1,which=3) xyplot(pr4,layout=c(5,1),as.table=TRUE) splom(pr4) ## used to fail because of NAs nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, Orange, start = c(Asym = 200, xmid = 725, scal = 350)) if (FALSE) { ## not working yet: detecting (slightly) lower deviance; not converging in 10k pr5 <- profile(nm1,which=1,verbose=1,maxmult=1.2) xyplot(.zeta~.focal|.par,type=c("l","p"),data=lme4:::as.data.frame.thpr(pr5), scale=list(x=list(relation="free")), as.table=TRUE) } } ## testLevel > 2 if (testLevel > 3) { fm3ML <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, REML=FALSE) ## ~ 4 theta-variables (+ 2 fixed), 19 seconds | 2018-05: 7.4" print(system.time(pr3 <- profile(fm3ML))) print(xyplot(pr3)) print(splom(pr3)) if (testLevel > 4) { if(requireNamespace("mlmRev")) { data("Contraception", package="mlmRev") ## fit already takes ~ 3 sec (2018-05) fm2 <- glmer(use ~ urban+age+livch + (urban|district), Contraception, binomial) print(system.time(pr5 <- profile(fm2,verbose=10))) # 2018-05: 462 sec = 7'42" ## -> 5 warnings notably "non-monotonic profile for .sig02" (the RE's corr.) print(xyplot(pr5)) } } ## testLevel > 4 } ## testLevel > 3 library("parallel") if (detectCores()>1) { p0 <- profile(fm1, which="theta_") ## http://stackoverflow.com/questions/12983137/how-do-detect-if-travis-ci-or-not travis <- nchar(Sys.getenv("TRAVIS")) > 0 if(.Platform$OS.type != "windows" && !travis) { prof01P <- profile(fm1, which="theta_", parallel="multicore", ncpus=2) stopifnot(all.equal(p0,prof01P)) } ## works in Solaris from an interactive console but not ??? ## via R CMD BATCH if (Sys.info()["sysname"] != "SunOS" && !travis) { prof01P.snow <- profile(fm1, which="theta_", parallel="snow", ncpus=2) stopifnot(all.equal(p0,prof01P.snow)) } } ## test profile/update from within functions foo <- function() { gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) ## return profile(gm1, which="theta_") } stopifnot(inherits(foo(), "thpr")) } ## testLevel>1 lme4/tests/elston.R0000644000176200001440000000706615022107260013710 0ustar liggesusers## original code for reading/aggregating: ## tickdata <- read.table("Elston2001_tickdata.txt",header=TRUE, ## colClasses=c("factor","numeric","factor","numeric","factor","factor")) ## tickdata <- transform(tickdata,cHEIGHT=scale(HEIGHT,scale=FALSE)) ## for (i in names(tickdata)) { ## if (is.factor(tickdata[[i]])) { ## tickdata[[i]] <- factor(tickdata[[i]],levels=sort(as.numeric(levels(tickdata[[i]])))) ## } ## } ## summary(tickdata) ## grouseticks <- tickdata ## library(reshape) ## meantick <- rename(aggregate(TICKS~BROOD,data=tickdata,FUN=mean), ## c(TICKS="meanTICKS")) ## vartick <- rename(aggregate(TICKS~BROOD,data=tickdata,FUN=var), ## c(TICKS="varTICKS")) ## uniqtick <- unique(subset(tickdata,select=-c(INDEX,TICKS))) ## grouseticks_agg <- Reduce(merge,list(meantick,vartick,uniqtick)) ## save("grouseticks","grouseticks_agg",file="grouseticks.rda") if (.Platform$OS.type != "windows") { library(lme4) data(grouseticks) do.plots <- FALSE form <- TICKS~YEAR+HEIGHT+(1|BROOD)+(1|INDEX)+(1|LOCATION) ## fit with lme4 ## library(lme4) ## t1 <- system.time(full_mod1 <- glmer(form, family="poisson",data=grouseticks)) ## c1 <- c(fixef(full_mod1),unlist(VarCorr(full_mod1)), logLik=logLik(full_mod1),time=t1["elapsed"]) ## allcoefs1 <- c(unlist(full_mod1@ST),fixef(full_mod1)) ## detach("package:lme4") ## lme4 summary results: t1 <- structure(c(1.288, 0.048, 1.36, 0, 0), class = "proc_time", .Names = c("user.self", "sys.self", "elapsed", "user.child", "sys.child")) c1 <- structure(c(11.3559080756861, 1.1804105508475, -0.978704335712111, -0.0237607330254979, 0.293232458048324, 0.562551624933584, 0.279548178949372, -424.771990224991, 1.36), .Names = c("(Intercept)", "YEAR96", "YEAR97", "HEIGHT", "INDEX", "BROOD", "LOCATION", "logLik", "time.elapsed" )) allcoefs1 <- structure(c(0.541509425632023, 0.750034415832756, 0.528723159081737, 11.3559080756861, 1.1804105508475, -0.978704335712111, -0.0237607330254979 ), .Names = c("", "", "", "(Intercept)", "YEAR96", "YEAR97", "HEIGHT")) pars <- function(x) unlist(getME(x,c("theta","beta"))) if (lme4:::testLevel() > 1) { t2 <- system.time(full_mod2 <- glmer(form, family="poisson",data=grouseticks)) ##>> 2 x checkConv(.. "derivs") warning: 1. failed to conv. 2. nearly unidentifiable c2 <- c(fixef(full_mod2), unlist(VarCorr(full_mod2)), logLik = logLik(full_mod2), time= t2["elapsed"]) ## refit ## FIXME: eventually would like to get _exactly_ identical answers on refit() full_mod3 <- refit(full_mod2, grouseticks$TICKS) print( all.equal(pars(full_mod2), pars(full_mod3), tolerance=0))# -> 1.2e-5 stopifnot(all.equal(pars(full_mod2), pars(full_mod3), tolerance=8e-5)) } ## deviance function ## FIXME: does compDev do _anything_ any more? mm <- glmer(form, family="poisson", data=grouseticks, devFunOnly=TRUE) mm2 <- glmer(form, family="poisson", data=grouseticks, devFunOnly=TRUE,control=glmerControl(compDev=TRUE)) stopifnot(all.equal(1780.5427072, mm(allcoefs1), tol = 1e-7)) } ## skip on windows (for speed) lme4/tests/priorWeightsModComp.R0000644000176200001440000000703515022107260016345 0ustar liggesuserslibrary(lme4) n <- nrow(sleepstudy) op <- options(warn = 1, # show as they happen ("false" convergence warnings) useFancyQuotes = FALSE) if (.Platform$OS.type != "windows") { ##' remove all attributes but names dropA <- function(x) `attributes<-`(x, list(names = names(x))) ##' transform result of "numeric" all.equal.list() to a named vector all.eqL <- function(x1, x2, ...) { r <- sub("^Component ", '', all.equal(x1, x2, tolerance = 0, ...)) r <- strsplit(sub(": Mean relative difference:", "&&", r), split="&&", fixed=TRUE) setNames(as.numeric(vapply(r, `[`, "1.234", 2L)), ## drop surrounding "..." nm = sub('"$', '', substring(vapply(r, `[`, "nam", 1L), first=2))) } seedF <- function(s) { if(s %in% c(6, 39, 52, 57, 63, 74, 76, 86)) switch(as.character(s) , "52"=, "63"=, "74" = 2 , "6"=, "39" = 3 , "86" = 8 # needs 4 on Lnx-64b , "76" = 70 # needs 42 on Lnx-64b , "57" = 90 # needs 52 on Lnx-64b ) else if(s %in% c(1, 12, 15, 34, 36, 41, 42, 43, 49, 55, 59, 67, 80, 85)) ## seeds 41,59, .. 15 1.0 else ## seeds 22, 20, and better 0.25 } ## be fast, running only 10 seeds by default: sMax <- if(lme4:::testLevel() > 1) 99L else 9L mySeeds <- 0L:sMax lapply(setNames(,mySeeds), function(seed) { cat("\n------ random seed =", seed, "---------\n") set.seed(seed) v <- rpois(n,1) + 1 w <- 1/v cat("weights w:\n") fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy, REML=FALSE, weights = w); cat("..2:\n") fm2 <- lmer(Reaction ~ Days + (1 | Subject), sleepstudy, REML=FALSE, weights = w) cat("weights w*10:\n") fm1.10 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy, REML=FALSE, weights = w*10);cat("..2:\n") fm2.10 <- lmer(Reaction ~ Days + (1 | Subject), sleepstudy, REML=FALSE, weights = w*10) ## ano12... <- dropA(anova(fm1, fm2 )) ano12.10 <- dropA(anova(fm1.10, fm2.10)) print(aEQ <- all.eqL(ano12..., ano12.10)) # showing differences if(!exists("notChisq")) notChisq <<- local({ n <- names(ano12...) grep("Chisq", n, value=TRUE, fixed=TRUE, invert=TRUE) }) stopifnot( all.equal(ano12...$Chisq, ano12.10$Chisq, tol = 1e-6 * seedF(seed)) , all.equal(ano12...[notChisq], ano12.10[notChisq], tol= 1.5e-8 * seedF(seed)) ) aEQ }) -> rallEQ cat("=====================================\n") rallEQ <- t(simplify2array(rallEQ)) notChisq <- intersect(notChisq, colnames(rallEQ)) ## sort according to "severity": srallEQ <- rallEQ[with(as.data.frame(rallEQ), order(AIC, Chisq)), ] round(log10(srallEQ), 2) saveRDS(srallEQ, "priorWeightsMod_relerr.rds") if(!dev.interactive(orNone=TRUE)) pdf("priorWeightsMod_relerr.pdf") matplot(mySeeds, log10(srallEQ), type="l", xlab=NA) ; grid() legend("topleft", ncol=3, bty="n", paste(1:6, colnames(srallEQ), sep = ": "), col=1:6, lty=1:6) tolD <- sqrt(.Machine$double.eps) # sqrt(eps_C) abline(h = log10(tolD), col = "forest green", lty=3) axis(4, at=log10(tolD), label=quote(sqrt(epsilon[c])), las=1) LRG <- which(srallEQ[,"AIC"] > tolD) if (length(LRG)>0) { text(LRG, log10(srallEQ[LRG, "AIC"]), names(LRG), cex = .8) } ## how close are we .. str(tF <- sapply(mySeeds, seedF)) round(sort( rallEQ[, "Chisq"] / (tF * 1e-6 ), decreasing=TRUE), 1) round(sort(apply(rallEQ[,notChisq] / (tF * 1.5e-8), 1, max), decreasing=TRUE), 1) } ## skip on windows (for speed) options(op) lme4/tests/lme4_nlme.R0000644000176200001440000000302215022107260014244 0ustar liggesusersif (lme4:::testLevel() > 1 || .Platform$OS.type != "windows") withAutoprint({ ## testing whether lme4 and nlme play nicely. Only known issue ## is lmList-masking ... library("lme4") library("nlme") fm1_lmer <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) fm1_lme <- lme (Reaction ~ Days, random = ~Days|Subject, sleepstudy) ## variance-covariance matrices: annoyingly different structures vc_lmer <- VarCorr(fm1_lmer) vc_lme <- VarCorr(fm1_lme, rdig = 8) suppressWarnings(storage.mode(vc_lme) <- "numeric")# 2 NAs vc_lmerx <- c(diag(vc_lmer[[1]]), attr(vc_lmer[[1]],"correlation")[1,2]) vc_lmex <- c( vc_lme[1:2,1], vc_lme[2,3]) stopifnot( all.equal(vc_lmex, vc_lmerx, tolerance= 4e-4) # had 3e-5, now see 0.000296 , ## fixed effects (much easier) : all.equal(fixef(fm1_lmer), fixef(fm1_lme)) # 3.6e-15 , all.equal(unname(unlist(unclass(ranef(fm1_lmer)))), unname(unlist(unclass(ranef(fm1_lme)))), tolerance = 2e-4) # had 2e-5, now see 8.41e-5 ) fm1L_lme <- nlme::lmList(distance ~ age | Subject, Orthodont) fm1L_lmer <- lme4::lmList(distance ~ age | Subject, Orthodont) stopifnot(all.equal(fixef(fm1L_lmer), fixef(fm1L_lme))) sm1L_e <- summary(fm1L_lme) sm1L_er <- summary(fm1L_lmer) stopifnot( all.equal(coef(sm1L_e), coef(sm1L_er), tol=1e-12)# even tol=0 works on some Lnx 64b ) ## FIXME: test opposite order }) lme4/tests/AAAtest-all.R0000644000176200001440000000154615022107260014431 0ustar liggesusersif (base::require("testthat", quietly = TRUE)) { pkg <- "lme4" require(pkg, character.only=TRUE, quietly=TRUE) if(getRversion() < "3.5.0") { withAutoprint <- identity ; prt <- print } else { prt <- identity } if(Sys.getenv("USER") %in% c("maechler", "bbolker")) withAutoprint({ ## for developers' sake: lP <- .libPaths() # ---- .libPaths() : ---- prt(lP) ## ---- Entries in .libPaths()[1] : ---- prt(list.files(lP[1], include.dirs=TRUE)) prt(sessionInfo()) prt(packageDescription("Matrix")) ## 'lme4' from packageDescription "file" : prt(attr(packageDescription("lme4"), "file")) }) test_check(pkg) ##======== ^^^ print(warnings()) # TODO? catch most of these by expect_warning(..) } else { cat( "package 'testthat' not available, cannot run unit tests\n" ) } lme4/tests/testthat/0000755000176200001440000000000015113144725014120 5ustar liggesuserslme4/tests/testthat/test-utils.R0000644000176200001440000000420215103764661016364 0ustar liggesusers## use old (<=3.5.2) sample() algorithm if necessary if ("sample.kind" %in% names(formals(RNGkind))) { suppressWarnings(RNGkind("Mersenne-Twister", "Inversion", "Rounding")) } #context("Utilities (including *non*-exported ones)") test_that("namedList", { nList <- lme4:::namedList a <- b <- c <- 1 expect_identical(nList(a,b,c), list(a = 1, b = 1, c = 1)) expect_identical(nList(a,b,d=c),list(a = 1, b = 1, d = 1)) expect_identical(nList(a, d=pi, c), list(a = 1, d = pi, c = 1)) }) test_that("Var-Cov factor conversions", { ## from ../../R/vcconv.R mlist2vec <- lme4:::mlist2vec Cv_to_Vv <- lme4:::Cv_to_Vv Cv_to_Sv <- lme4:::Cv_to_Sv Sv_to_Cv <- lme4:::Sv_to_Cv Vv_to_Cv <- lme4:::Vv_to_Cv ## set.seed(1); cvec1 <- sample(10, 6) v1 <- Cv_to_Vv(cvec1) expect_equal(unname(v1), structure(c(9, 12, 15, 65, 34, 93), clen = 3)) expect_equal(2, as.vector(Vv_to_Cv(Cv_to_Vv(2)))) expect_equivalent(c(v1, 1), Cv_to_Vv(cvec1, s=3) / 3^2) expect_equal(as.vector(ss1 <- Sv_to_Cv(Cv_to_Sv(cvec1))), cvec1) expect_equal(as.vector(vv1 <- Vv_to_Cv(Cv_to_Vv(cvec1))), cvec1) ## for length-1 matrices, Cv_to_Sv should be equivalent ## to multiplying Cv by sigma and appending sigma .... clist2 <- list(matrix(1),matrix(2),matrix(3)) cvec2 <- mlist2vec(clist2) expect_equal(cvec2, structure(1:3, clen = rep(1,3)), tolerance=0) expect_true(all((cvec3 <- Cv_to_Sv(cvec2, s=2)) == c(cvec2*2,2))) n3 <- length(cvec3) expect_equivalent(Sv_to_Cv(cvec3, n=rep(1,3), s=2), cvec3[-n3]/cvec3[n3]) }) ## moved to lme4 test_that("getData", { ## test what happens when wrong version of 'data' is found in environment of formula ... f <- round(Reaction) ~ 1 + (1|Subject) g <- function() { data <- sleepstudy m1 <- glmer(f, data = data, family = poisson) } m1 <- g() expect_error(getData(m1), "object found is not a data frame or matrix") p1 <- suppressMessages(predict(m1, newparams = list(beta = 5, theta = 1), type = "response", re.form = ~ 1|Subject)) expect_equal(unname(head(p1, 2)), c(444.407382298401, 444.407382298401)) }) lme4/tests/testthat/test-allFit.R0000644000176200001440000001134415103764661016444 0ustar liggesuserstestLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 if (testLevel>1) { L <- load(system.file("testdata", "lme-tst-fits.rda", package="lme4", mustWork=TRUE)) had_pars <- exists("pars", envir = globalenv(), inherits = FALSE) had_ctrl <- exists("ctrl", envir = globalenv(), inherits = FALSE) gm_all <- allFit(fit_cbpp_1, verbose=TRUE) gm_all_nostart <- allFit(fit_cbpp_1, verbose=FALSE, start_from_mle = FALSE) test_that("pars and ctrl did not leak into the environment", { expect_equal(exists("pars", envir = globalenv(), inherits = FALSE), had_pars) expect_equal(exists("ctrl", envir = globalenv(), inherits = FALSE), had_ctrl) }) summary(gm_all)$times[,"elapsed"] summary(gm_all_nostart)$times[,"elapsed"] sapply(gm_all, function(x) x@optinfo$feval) sapply(gm_all_nostart, function(x) x@optinfo$feval) ## library(microbenchmark) ## mb1 <- microbenchmark( ## start = allFit(fit_cbpp_1, verbose=FALSE), ## nostart = allFit(fit_cbpp_1, verbose=FALSE, start_from_mle = FALSE) ## ) test_that("allFit print/summary is fine", { expect_is(gm_all, "allFit") expect_is(summary(gm_all), "summary.allFit") }) test_that("nloptwrap switches optimizer correctly", { expect_equal(attr(gm_all[["nloptwrap.NLOPT_LN_BOBYQA"]],"optCtrl"), list(maxeval = 1e5, algorithm = "NLOPT_LN_BOBYQA")) expect_equal(attr(gm_all[["nloptwrap.NLOPT_LN_NELDERMEAD"]],"optCtrl"), list(maxeval = 1e5, algorithm = "NLOPT_LN_NELDERMEAD")) }) test_that("lmerControl() arg works too", { fm0 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) fm <- update(fm0, control = lmerControl(optCtrl = list(xtol_rel = 1e-8, ftol_rel = 1e-8), calc.derivs=FALSE)) afm0 <- allFit(fm0,verbose=FALSE) afm <- allFit(fm,verbose=FALSE) # used to fail drop_ <- function(x) { x[setdiff(names(x), c("times","feval"))] } ## should be approximately the same expect_equal(drop_(summary(afm0)), drop_(summary(afm)), tolerance = 1e-2) ## should NOT be the same! expect_false(isTRUE(all.equal(drop_(summary(afm0)), drop_(summary(afm)), tolerance=1e-10))) }) test_that("glmerControl() arg + optimizer", { ## GH #523? fit_cbpp_1u <- update(fit_cbpp_1, control=glmerControl(optimizer="nloptwrap", optCtrl=list(xtol_abs=1e-10, ftol_abs=1e-10))) af2 <- allFit(fit_cbpp_1u, verbose=FALSE) expect_equal(class(af2),"allFit") }) test_that("i in model call is OK", { ## GH #538 ## ugh, testthat scoping is insane ... ## if d and i are ## assigned normally with <- outside expect_true(), test fails ## BUT global assignment of 'd' breaks downstream tests in ## 'data= argument and formula evaluation' (test-formulaEval.R) ## ddd breaks similar test in 'fitting lmer models' (test-lmer.R) ## (where 'd' is supposed to be nonexistent) ## if we do global assignment with <<- ## can't figure out how to remove d (or ddd) after it's created to leave ## the environment clean ... ## tried to encapsulate all the necessary assignments ## within expect_true({ ... }) but that fails in other ways nr <- nrow(sleepstudy) ..dd <<- list(sleepstudy[1:nr,], sleepstudy[-(1:nr)]) i <<- 1 fm0 <- lmer(Reaction ~ Days + (1 | Subject), data=..dd[[i]]) aa <- allFit(fm0, verbose=FALSE) expect_true( all(summary(aa)$which.OK) ) }) test_that("allFit/update scoping", { ## GH #601 fit_func <- function(dataset) { gm1 <- glmer( cbind(incidence, size - incidence) ~ period + (1 | herd), data = dataset, family = binomial ) gm1@call$data <- dataset allFit(gm1, catch.errs=FALSE) } cc <- capture.output(ff <- fit_func(cbpp)) expect_true(all(summary(ff)$which.OK)) }) test_that("maxfun works", { gm_it10 <- suppressWarnings(allFit(fit_cbpp_1, verbose=FALSE, maxfun = 10)) v <- vapply(gm_it10, function(x) as.integer(x@optinfo$feval), FUN.VALUE=1L) ## function values are sometimes off a bit (due to initialization or Hessian calculation) ## but close enough ... expect_true(all(is.na(v) | v < 12)) }) } ## testLevel lme4/tests/testthat/test-catch.R0000644000176200001440000000101315103764661016303 0ustar liggesusers#context("storing warnings, convergence status, etc.") test_that("storewarning", { gCtrl <- glmerControl(optimizer = "Nelder_Mead", optCtrl = list(maxfun=3)) expect_warning(gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data=cbpp, family=binomial, control=gCtrl), "failure to converge in 3") expect_equal(gm1@optinfo$warnings[[1]],"failure to converge in 3 evaluations") ## FIXME: why is conv==0 here? }) lme4/tests/testthat/test-nlmer.R0000644000176200001440000000070315103764661016343 0ustar liggesuserstestLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 #context("lower/upper bounds on nlmer models") test_that("nlmer", { startvec <- c(Asym = 200, xmid = 725, scal = 350) nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, Orange, start = startvec, control=nlmerControl(optCtrl=list(lower=c(0,200,-Inf,-Inf)))) expect_equal(unname(fixef(nm1)[1]),200) }) lme4/tests/testthat/test-glmer.R0000644000176200001440000004461715103764661016350 0ustar liggesuserssource(system.file("testdata", "lme-tst-funs.R", package="lme4", mustWork=TRUE))# -> uc() [back-compatible c()] testLevel <- lme4:::testLevel() gives_error_or_warning <- function (regexp = NULL, all = FALSE, ...) { function(expr) { res <- try(evaluate_promise(expr),silent=TRUE) no_error <- !inherits(res, "try-error") if (no_error) { warnings <- res$warnings if (!is.null(regexp) && length(warnings) > 0) { return(matches(regexp, all = FALSE, ...)(warnings)) } else { return(expectation(length(warnings) > 0, "no warnings or errors given", paste0(length(warnings), " warnings created"))) } } if (!is.null(regexp)) { return(matches(regexp, ...)(res)) } else { expectation(TRUE, "no error thrown", "threw an error") } } } ## expect_that(stop("foo"),gives_error_or_warning("foo")) ## expect_that(warning("foo"),gives_error_or_warning("foo")) ## expect_that(TRUE,gives_error_or_warning("foo")) ## expect_that(stop("bar"),gives_error_or_warning("foo")) ## expect_that(warning("bar"),gives_error_or_warning("foo")) if(testLevel > 1) { #context("fitting glmer models") test_that("glmer", { set.seed(101) d <- data.frame(z=rbinom(200,size=1,prob=0.5), f=factor(sample(1:10,200,replace=TRUE))) ## Using 'method=*' defunct in 2019-05 (after 6 years of deprecation) ## expect_warning(glmer(z~ 1|f, d, family=binomial, method="abc"),"Use the nAGQ argument") ## expect_warning(glmer(z~ 1|f, d, family=binomial, method="Laplace"),"Use the nAGQ argument") ##sp expect_warning(glmer(z~ 1|f, d, sparseX=TRUE),"has no effect at present") expect_that(gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial), is_a("glmerMod")) expect_that(gm1@resp, is_a("glmResp")) expect_that(gm1@pp, is_a("merPredD")) expect_equal(ge1 <- unname(fixef(gm1)), c(-1.39854982537216, -0.992335519118859, -1.12867532780426, -1.58030423764517), tolerance=5e-4) expect_equal(c(VarCorr(gm1)[[1]]), 0.41245527438386, tolerance=6e-4) ### expect_that(family(gm1), equals(binomial())) ### ?? binomial() has an 'initialize' component ... and the order is different expect_equal(deviance(gm1), 73.47428, tolerance=1e-5) ## was -2L = 184.05267459802 expect_equal(sigma(gm1), 1) expect_equal(extractAIC(gm1), c(5, 194.052674598026), tolerance=1e-5) expect_equal(theta <- unname(getME(gm1, "theta")), 0.642226809144453, tolerance=6e-4) expect_that(X <- getME(gm1, "X"), is_equivalent_to( model.matrix(model.frame(~ period, data=cbpp), cbpp))) expect_that(Zt <- getME(gm1, "Zt"), is_a("dgCMatrix")) expect_equal(dim(Zt), c(15L, 56L)) expect_equal(Zt@x, rep.int(1, 56L)) expect_that(Lambdat <- getME(gm1, "Lambdat"), is_a("dgCMatrix")) expect_equivalent(as(Lambdat, "matrix"), diag(theta, 15L, 15L)) expect_is(gm1_probit <- update(gm1,family=binomial(link="probit")),"merMod") expect_equal(family(gm1_probit)$link,"probit") ## FIXME: test user-specified/custom family? expect_error(glFormula(cbind(incidence, size - incidence) ~ period + (1 | herd), data = subset(cbpp, herd==levels(herd)[1]), family = binomial), "must have > 1") expect_warning(glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = subset(cbpp, herd %in% levels(herd)[1:4]), family = binomial, control=glmerControl(check.nlev.gtreq.5="warning")), "< 5 sampled levels") expect_warning(fm1. <- glmer(Reaction ~ Days + (Days|Subject), sleepstudy), regexp="calling .* with family=gaussian .* as a shortcut") options(warn=2) options(glmerControl=list(junk=1,check.conv.grad="ignore")) expect_warning(glmer(z~ 1|f, d, family=binomial), "some options") options(glmerControl=NULL) cbppX <- transform(cbpp,prop=incidence/size) expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, start=NULL), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, verbose=0L), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, subset=TRUE), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, na.action="na.exclude"), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, offset=rep(0,nrow(cbppX))), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, contrasts=NULL), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, devFunOnly=FALSE), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, control=glmerControl(optimizer="Nelder_Mead")), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, control=glmerControl()), "glmerMod") options(warn=0) expect_error(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, junkArg=TRUE), "unused argument") if(FALSE) { ## Hadley broke this expect_warning(glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial, control=list()), "instead of passing a list of class") expect_warning(glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial, control=lmerControl()), "instead of passing a list of class") } ## load(system.file("testdata","radinger_dat.RData",package="lme4")) mod <- glmer(presabs~predictor+(1|species),family=binomial, radinger_dat) expect_is(mod,"merMod") ## tolerance: 32-bit Windows (CRAN) reported ave.diff of 5.33e-8 ## 64-bit Win-builder r73242 now reports ave. diff of 1.31e-5 ... expect_equal(unname(fixef(mod)), c(0.5425528,6.4289962), tolerance = 1e-4) set.seed(101) ## complete separation case d <- data.frame(y=rbinom(1000,size=1,p=0.5), x=runif(1000), f=factor(rep(1:20,each=50)), x2=rep(0:1,c(999,1))) expect_message(mod2 <- glmer(y~x+x2+(1|f),data=d,family=binomial, control=glmerControl(check.conv.hess="ignore", check.conv.grad="ignore")), "singular") expect_equal(unname(fixef(mod2))[1:2], c(-0.10036244,0.03548523), tolerance=1e-4) expect_true(unname(fixef(mod2)[3] < -10)) expect_message(mod3 <- update(mod2, family=binomial(link="probit")), "singular") # singular Hessian warning expect_equal(unname(fixef(mod3))[1:2], c(-0.062889, 0.022241), tolerance=1e-4) expect_true(fixef(mod3)[3] < -4) expect_message(mod4 <- update(mod2, family=binomial(link="cauchit"), control=glmerControl(check.conv.hess="ignore", check.conv.grad="ignore")))#--> singular Hessian warning ## on-the-fly creation of index variables if (FALSE) { ## FIXME: fails in testthat context -- 'd' is not found ## in the parent environment of glmer() -- but works fine ## otherwise ... set.seed(101) d <- data.frame(y1=rpois(100,1), x=rnorm(100), ID=1:100) fit1 <- glmer(y1 ~ x+(1|ID),data=d,family=poisson) fit2 <- update(fit1, .~ x+(1|rownames(d))) expect_equal(unname(unlist(VarCorr(fit1))), unname(unlist(VarCorr(fit2)))) } ## if(testLevel > 2) { load(system.file("testdata","mastitis.rda",package="lme4")) t1 <- system.time(g1 <- suppressWarnings(glmer(NCM ~ birth + calvingYear + (1|sire) + (1|herd), mastitis, poisson, ## current (2014-04-24) default: --> Warning control=glmerControl( # max|grad| = 0.021 .. optimizer=c("bobyqa","Nelder_Mead"))))) t2 <- system.time(g2 <- suppressWarnings(update(g1, control=glmerControl(optimizer="bobyqa")))) ## rbind(t1,t2)[,"elapsed"] ## 20 (then 13.0) seconds N-M vs 8 (then 4.8) seconds bobyqa ... ## print(t1[3] / t2[3]) # 0.37; => 1.25 should be on the safe side expect_lte(t2[3], 1.25 * t1[3]) ## problem is fairly ill-conditioned so parameters ## are relatively far apart even though likelihoods are OK expect_equal(logLik(g1),logLik(g2),tolerance=2e-7) } ## test bootstrap/refit with nAGQ>1 gm1AGQ <- update(gm1,nAGQ=2) s1 <- simulate(gm1AGQ) expect_equal(attr(bootMer(gm1AGQ,fixef),"bootFail"),0) ## do.call(new,...) bug new <- "foo" expect_that(gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial), is_a("glmerMod")) rm("new") ## test issue #47, from Wolfgang Viechtbauer ## create some data n <- 100 ai <- rep(0:1, each = n/2) bi <- 1-ai ci <- c(rep(0,42), rep(1,8), rep(0,18), rep(1,32)) di <- 1-ci event <- c(rbind(ai,ci)) group <- rep(c(1,0), times=n) id <- rep(1:n, each=2) gm3 <- glmer(event ~ group + (1 | id), family=binomial, nAGQ=21) sd3 <- sqrt(diag(vcov(gm3))) expect_equal(uc(`(Intercept)` = 0.42542855, group = 0.42492581), sd3, tolerance=1e-5) # 7e-9 {Lnx} ## unfortunately these answers aren't reliably "wrong" any more (2024-07-02 Pop!OS Linux) expect_warning(vcov(gm3, use.hessian=FALSE), "finite-difference Hessian") expect_equal(suppressWarnings(sqrt(diag(vcov(gm3,use.hessian=FALSE)))), uc(`(Intercept)` = 0.3840921, group = 0.3768747), tolerance=1e-7) # 6.5e-8 expect_equal(sd3, unn(coef(summary(gm3))[,"Std. Error"])) ## test non-pos-def finite-difference Hessian ... if(getRversion() > "3.0.0") { ## saved fits are not safe with old R versions L <- load(system.file("testdata","polytomous_vcov_ex.RData", package="lme4", mustWork=TRUE)) expect_warning(vcov(polytomous_vcov_ex),"falling back to var-cov") } ## damage Hessian to make it singular ## (example thanks to J. Dushoff) gm1H <- gm1 gm1H@optinfo$derivs$Hessian[5,] <- 0 expect_warning(vcov(gm1H),"falling back to var-cov") ## test convergence warnings L <- load(system.file("testdata","gopherdat2.RData", package="lme4", mustWork=TRUE)) g0 <- glmer(shells~prev + (1|Site)+offset(log(Area)), family=poisson, data=Gdat) ## fit year as factor: OK gc <- glmerControl(check.conv.grad="stop") expect_is(update(g0,.~.+factor(year), control=gc), "glmerMod") ## error/warning with year as numeric: ## don't have full knowledge of which platforms lead to which ## results, and can't detect whether we're running on valgrind, ## which changes the result on 32-bit linux ... ## SEGFAULT on MacOS? why? if (FALSE) { expect_that(update(g0,.~.+year), gives_error_or_warning("(failed to converge|pwrssUpdate did not converge)")) } ## ("(failed to converge|pwrssUpdate did not converge in)")) ## if (sessionInfo()$platform=="i686-pc-linux-gnu (32-bit)") { ## expect_warning(update(g0, .~. +year), "failed to converge") ## } else { ## ## MacOS x86_64-apple-darwin10.8.0 (64-bit) ## ## MM's platform ## ## "pwrssUpdate did not converge in (maxit) iterations" ## expect_error(update(g0, .~. +year), "pwrssUpdate did not converge in") ## } ## OK if we scale & center it expect_is(update(g0,.~. + scale(year), control=gc), "glmerMod") ## not OK if we scale and don't center expect_warning(update(g0,.~. + scale(year,center=FALSE)), "failed to converge with max|grad|") ## OK if center and don't scale expect_is(update(g0,.~. + scale(year,center=TRUE,scale=FALSE), control=gc), "glmerMod") ## try higher-order AGQ expect_is (update(gm1,nAGQ=90), "glmerMod") expect_error(update(gm1,nAGQ=101),"ord < 101L") ## non-numeric response variables ss <- transform(sleepstudy, Reaction = as.character(Reaction)) expect_error(glmer(Reaction~(1|Days), family="poisson", data=ss), "response must be numeric") expect_error(glmer(Reaction~(1|Days), family="binomial", data=ss), "response must be numeric or factor") ss2 <- transform(ss,rr=rep(c(TRUE,FALSE),length.out=nrow(ss))) ## should work OK with logical too expect_is(glmer(rr~(1|Days),family="binomial",data=ss2),"merMod") ## starting values with log(.) link -- thanks to Eric Weese @ Yale: grp <- rep(letters[1:5], 20); set.seed(1); x <- rnorm(100) expect_error(glmer(x ~ 1 + (1|grp), family=gaussian(link="log")), "valid starting values") ## related to GH 231 ## fails on some platforms, skip for now if (FALSE) { rr <- gm1@resp$copy() ff <- setdiff(ls(gm1@resp),c("copy","initialize","initialize#lmResp","ptr", "updateMu","updateWts","resDev","setOffset","wrss")) for (i in ff) { expect_equal(gm1@resp[[i]],rr[[i]]) } } ## bad start case load(system.file("testdata","fakesim.RData",package="lme4")) rfit <- glmer(Inew/S ~ R0-1 + offset(log(I/N)) + (1|R0:trial) , family=binomial(link="cloglog") , data=dat , weight=S , control=glmerControl(optimizer="bobyqa", nAGQ0initStep=FALSE) , start = list(fixef=c(0,0,0),theta=1)) expect_equal(exp(fixef(rfit)), c(R01 = 1.2735051, R02 = 2.0330635, R03 = 2.9764088), tolerance=1e-5) ## gaussian with log link and zero values in response ... ## fixed simulation code, passing mustart properly dd <- expand.grid(x = seq(-2,3,length.out=10), f = factor(1:10)) dd$y <- simulate(~x+(1|f), family=gaussian(link="log"), newdata=dd, newparams=list(beta=c(0,1),theta=1,sigma=1), seed=101)[[1]] dd$y <- pmax(dd$y,0) expect_error(glmer (y ~ x + (1|f), family = gaussian(link="log"), data=dd),"cannot find valid starting values") g1 <- glmer (y ~ x + (1|f), family = gaussian(link="log"), data=dd, mustart=pmax(dd$y,0.1)) msum <- c(fixef(g1),unlist(c(VarCorr(g1))),c(logLik(g1))) expect_equal(msum, c(`(Intercept)` = 0.23389405, x = 1.0017436, f = 0.24602992, -156.7773), tolerance=1e-5) ## GH 415 expect_warning(glmer (round(Reaction) ~ Days + (1|Subject), data=sleepstudy[1:100,], family=poisson, control=lmerControl()), "please use glmerControl") }) } ## testlevel>1 test_that("glmer with etastart", { ## make sure etastart is passed through ## (fixed in commit b6fb1ac83885ff06 but never tested?) m1 <- glmer(incidence/size ~ period + (1|herd), weights = size, family = binomial, data = cbpp) m1E <- update(m1, etastart = rep(1, nrow(cbpp))) expect_true(!identical(fixef(m1), fixef(m1E))) }) test_that("turn off conv checking for nobs > check.conv.nobsmax", { ## calc derivs and check convergence gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) nn <- nrow(cbpp)-1 ## neither derivs nor conv check gm2 <- update(gm1, control = glmerControl(check.conv.nobsmax = nn)) ## no conv check, do calc derivs gm3 <- update(gm1, control = glmerControl(check.conv.nobsmax = nn, calc.derivs = TRUE)) expect_null(gm2@optinfo$derivs) expect_false(is.null(gm1@optinfo$derivs)) expect_false(is.null(gm3@optinfo$derivs)) expect_equal(gm1@optinfo$conv$lme4, list()) expect_null(gm2@optinfo$conv$lme4) expect_null(gm3@optinfo$conv$lme4) }) test_that("turn off conv checking for npara > check.conv.nparmax", { ## Code suggestion from Claude ai: ## https://claude.ai/share/06aa5947-f447-4e08-b65b-92f36c4b19a9 set.seed(1) n_groups <- 50 n_per_group <- 20 n <- n_groups * n_per_group dat <- data.frame( group = rep(1:n_groups, each = n_per_group), x1 = rnorm(n), x2 = rnorm(n) ) set.seed(101) form <- y ~ 1 + x1 * x2 + (1|group) dat$y <- simulate(form[-2], ## one-sided formula newdata = dat, family = binomial, newparams = list(beta = c(-3, 2.5, 3, 1.5), theta = 2.5))[[1]] # note: maxfun had to be artificially low for convergence warnings... mod1 <- suppressWarnings( glmer(form, data = dat, family = binomial, control = glmerControl(optCtrl = list(maxfun = 100))) ) mod2 <- suppressWarnings( update(mod1, control = glmerControl(optCtrl = list(maxfun = 100), check.conv.nparmax = 2)) ) ## First should give a warning expect_false(is.null(mod1@optinfo$conv$lme4)) ## Second shouldn't be evaluated expect_null(mod2@optinfo$conv$lme4) }) lme4/tests/testthat/test-lmerResp.R0000644000176200001440000000431515103764661017022 0ustar liggesusers data(Dyestuff, package="lme4") n <- nrow(Dyestuff) ones <- rep.int(1, n) zeros <- rep.int(0, n) YY <- Dyestuff$Yield mYY <- mean(YY) #context("lmerResp objects") test_that("lmerResp", { mres <- YY - mYY rr <- lmerResp$new(y=YY) expect_that(rr$weights, equals(ones)) expect_that(rr$sqrtrwt, equals(ones)) expect_that(rr$sqrtXwt, equals(ones)) expect_that(rr$offset, equals(zeros)) expect_that(rr$mu, equals(zeros)) expect_that(rr$wtres, equals(YY)) expect_that(rr$wrss(), equals(sum(YY^2))) expect_that(rr$updateMu(rep.int(mYY, n)), equals(sum(mres^2))) expect_that(rr$REML, equals(0L)) rr$REML <- 1L expect_that(rr$REML, equals(1L)) }) mlYY <- mean(log(YY)) gmeanYY <- exp(mlYY) # geometric mean #context("glmResp objects") test_that("glmResp", { mres <- YY - gmeanYY gmean <- rep.int(gmeanYY, n) rr <- glmResp$new(family=poisson(), y=YY) expect_that(rr$weights, equals(ones)) expect_that(rr$sqrtrwt, equals(ones)) expect_that(rr$sqrtXwt, equals(ones)) expect_that(rr$offset, equals(zeros)) expect_that(rr$mu, equals(zeros)) expect_that(rr$wtres, equals(YY)) expect_that(rr$n, equals(ones)) ## wrss() causes an update of mu which becomes ones, wtres also changes expect_that(rr$wrss(), equals(sum((YY-1)^2))) expect_that(rr$mu, equals(ones)) expect_that(rr$wtres, equals(YY-ones)) expect_that(rr$updateMu(rep.int(mlYY, n)), equals(sum(mres^2))) expect_that(rr$mu, equals(gmean)) expect_that(rr$muEta(), equals(gmean)) expect_that(rr$variance(), equals(gmean)) rr$updateWts() expect_that(1/sqrt(rr$variance()), equals(rr$sqrtrwt)) expect_that(as.vector(rr$sqrtXwt), equals(rr$sqrtrwt * rr$muEta())) }) lme4/tests/testthat/test-stepHalving.R0000644000176200001440000000055015103764661017512 0ustar liggesusersload(system.file("testdata","survdat_reduced.Rda",package="lme4")) test_that('Step-halving works properly', { # this example is known to require step-halving (or at least has in the past # required step-halving) form <- survprop~(1|nobs) m <- glmer(form,weights=eggs,data=survdat_reduced,family=binomial,nAGQ=1L) expect_that(m, is_a("glmerMod")) }) lme4/tests/testthat/test-ranef.R0000644000176200001440000000736015103764661016327 0ustar liggesusersstopifnot(require("testthat"), require("lme4")) set.seed(101) n <- 500 d <- data.frame(x=rnorm(n), f=factor(sample(1:10,n,replace=TRUE), labels=LETTERS[1:10]), g=factor(sample(1:25,n,replace=TRUE), labels=letters[1:25])) d$y <- suppressMessages(simulate(~1+x+(1|f)+(x|g),family=binomial, newdata=d, newparams=list(beta=c(0,1), theta=c(1,1,2,1)))[[1]]) fm1 <- glmer(y~(1|f)+(x|g),family=binomial,data=d) #context("ranef") test_that("warn extra args", { expect_warning(ranef(fm1,transf=exp),"additional arguments") }) test_that("dotplot_ranef", { rr <- ranef(fm1,condVar=TRUE) expect_is(lattice::dotplot(rr,scales=list(x = list(relation = 'free')))$g, "trellis") expect_is(lattice::dotplot(rr,transf=exp, scales=list(x = list(relation = 'free')))$g, "trellis") expect_is(as.data.frame(rr),"data.frame") rr0 <- ranef(fm1) expect_is(as.data.frame(rr0),"data.frame") }) test_that("Dyestuff consistent with lme4.0", { lme4.0condVarDyestuff <- c(362.3583, 362.3583, 362.3583, 362.3583, 362.3583, 362.3583) fm <- lmer(Yield ~ 1|Batch, Dyestuff, REML=FALSE) lme4condVarDyestuff <- drop(attr(ranef(fm,condVar=TRUE)$Batch,"postVar")) expect_equal(lme4.0condVarDyestuff, lme4condVarDyestuff, tolerance = 1e-3) }) test_that("sleepstudy consistent with lme4.0", { lme4.0condVarsleepstudy <- matrix(c(145.71273, -21.440414, -21.44041, 5.310927), 2, 2) fm <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) lme4condVarsleepstudy <- attr(ranef(fm,condVar=TRUE)$Subject,"postVar")[,,1] expect_equal(lme4.0condVarsleepstudy, lme4condVarsleepstudy, tolerance = 2e-4) }) test_that("cbpp consistent with lme4.0", { lme4.0condVarcbpp <- c(0.12128867, 0.13363275, 0.08839850, 0.17337928, 0.12277914, 0.14436663, 0.10658333, 0.10309812, 0.21289738, 0.13740279, 0.09555677, 0.19460241, 0.14808316, 0.12631006, 0.15816769) gm <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) lme4condVarcbpp <- as.numeric(attr(ranef(gm,condVar=TRUE)$herd,"postVar")) expect_equal(lme4.0condVarcbpp, lme4condVarcbpp, tolerance = 1e-3) }) #context("multiple terms per factor") test_that("multiple terms work", { fm <- lmer(Reaction ~ Days + (1|Subject)+ (0+Days | Subject), sleepstudy, control=lmerControl(optimizer="nloptwrap", optCtrl=list(xtol_abs=1e-6, ftol_abs=1e-6))) rr <- ranef(fm, condVar=TRUE) expect_equal(as.data.frame(rr)[c(1,19),], structure( list(grpvar = c("Subject", "Subject"), term = structure(1:2, .Label = c("(Intercept)", "Days"), class = "factor"), grp = structure(c(9L, 9L), .Label = c("309", "310", "370", "349", "350", "334", "335", "371", "308", "369", "351", "332", "372", "333", "352", "331", "330", "337"), class = "factor"), condval = c(1.5116973008, 9.32373076098), condsd = c(12.238845590, 2.33546851406)), row.names = c(1L, 19L), class = "data.frame"), tolerance = 1e-5) cv <- attr(rr$Subject, "postVar") expect_equal(lapply(cv, drop), list(`(Intercept)` = rep(149.79166, 18), Days = rep(5.4543894, 18)), tolerance = 1e-4) }) lme4/tests/testthat/test-oldRZXfailure.R0000644000176200001440000000072215103764661017761 0ustar liggesusersload(system.file("testdata","crabs_randdata00.Rda",package="lme4")) test_that('RZX is being calculated properly', { # this is a test for an old problem, documented here: # http://stevencarlislewalker.github.io/notebook/RZX_problems.html fr <- cbind(final.snail.density, snails.lost) ~ crab.speciesS + crab.sizeS + crab.speciesS:crab.sizeS + (snail.size | plot) m <- glmer(fr, data = randdata00, family = binomial) expect_that(m, is_a("glmerMod")) }) lme4/tests/testthat/test-glmmFail.R0000644000176200001440000000263515103764661016764 0ustar liggesusersdata("sleepstudy", package = "lme4") source(system.file("testdata/lme-tst-funs.R", package="lme4", mustWork=TRUE)) ##-> gSim(), a general simulation function ... set.seed(101) dBc <- gSim(family=binomial(link="cloglog"), nbinom = 1) # {0,1} Binomial ## m1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), ## family = binomial, data = cbpp) #context("Errors and warnings from glmer") test_that("glmer", { expect_error(glmer(y ~ 1 + (1|block), data=dBc, family=binomial(link="cloglog")), "Response is constant") expect_error(glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp, REML=TRUE), "unused argument.*REML") expect_warning(glmer(Reaction ~ Days + (Days|Subject), sleepstudy), "calling glmer.*family=gaussian.*deprecated") expect_warning(glmer(Reaction ~ Days + (Days|Subject), sleepstudy, family=gaussian), "calling glmer.*family=gaussian.*deprecated") m3 <- suppressWarnings(glmer(Reaction ~ Days + (Days|Subject), sleepstudy)) m4 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) m5 <- suppressWarnings(glmer(Reaction ~ Days + (Days|Subject), sleepstudy, family=gaussian)) expect_equal(fixef(m3),fixef(m5)) m3@call[[1]] <- m5@call[[1]] <- quote(lmer) ## hack call expect_equal(m3,m4) expect_equal(m3,m5) }) lme4/tests/testthat/test-glmernb.R0000644000176200001440000000565415103764661016666 0ustar liggesuserstestLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 if (testLevel>1) { #context("glmer.nb") test_that("basic", { set.seed(101) dd <- expand.grid(f1 = factor(1:3), f2 = LETTERS[1:2], g=1:9, rep=1:15, KEEP.OUT.ATTRS=FALSE) mu <- 5*(-4 + with(dd, as.integer(f1) + 4*as.numeric(f2))) dd$y <- rnbinom(nrow(dd), mu = mu, size = 0.5) require("MASS") m.glm <- glm.nb(y ~ f1*f2, data=dd) m.nb <- glmer.nb(y ~ f1*f2 + (1|g), data=dd) expect_equal(unname(fixef(m.nb)), c(1.65008, 0.76715, 1.01147, 1.51241, -0.61506, -0.6104), tol=1e-5) expect_is(m.nb,"glmerMod") ## 'family' properly quoted/not expanded in call? expect_true(grepl("negative\\.binomial\\(theta *= *[0-9]*\\.[0-9]+\\)", deparse(m.nb@call$family))) expect_null(m.nb@call$verbose) ## check: GH #321 expect_equal(fixef(m.nb), coef (m.glm), tol=1e-5) ## GH #319 ## GH #285 m.nb1 <- glmer(Reaction > 250 ~ Days + (1|Subject), data = sleepstudy, family=poisson) ## previously failing on Travis-CI m.nb2 <- glmer.nb(y ~ f1*f2 + (1|g), data=dd, subset = g!=8) expect_equal(unname(ngrps(m.nb2)),8) ## expect parameters, ngrps *not* to equal full model expect_equal(unname(fixef(m.nb2)), c(1.629240234, 0.76028840, 1.008629913, 1.6172507, -0.6814426, -0.66468330),tol=1e-5) ## control handling ... this should suppress warnings ... old.opts <- options(warning=2) m.nb2 <- glmer.nb(round(Reaction) ~ Days + (1|Subject), data = sleepstudy, subset = Subject != 370, control=glmerControl(check.conv.grad="ignore")) expect_is(m.nb2,"glmerMod") options(old.opts) m.nb3 <- glmer.nb(y~f1+(1|g), data=dd, contrasts=list(f1=contr.sum)) ## make sure *different* fixed effects from previous fit ... expect_equal(fixef(m.nb3), structure(c(2.93061, -0.29779, 0.02586), .Names = c("(Intercept)", "f11", "f12")),tol=1e-5) ## make sure 'data' is in call even if unnamed m.nb4 <- glmer.nb(y~f1+(1|g), dd) expect_equal(names(m.nb4@call),c("","formula","data","family")) ## GH 322; allow offset m.nb2 <- glmer.nb(y~f1+(1|g), data=dd, offset=rep(0,nrow(dd))) }) if (requireNamespace("merDeriv")) { test_that("summary not broken by merDeriv", { library(merDeriv) data(Arabidopsis) mod <- glmer.nb(total.fruits~status+nutrient+(1|gen), data=Arabidopsis) ## trivial test of existence/non-failure ... expect_is(summary(mod), "summary.merMod") ## may? expose other problems as S3 methods won't be unloaded properly ... detach("package:merDeriv") }) } } ## testLevel > 1 lme4/tests/testthat/test-simulate_formula.R0000644000176200001440000000465215103764661020605 0ustar liggesusersquietly <- TRUE ## factory for making methods mk_method <- function(class, print_dims=FALSE) { method <- sprintf("simulate.formula_lhs_%s",class) sim_generic <- function(object, nsim=1, seed=NULL, ...) { if (!quietly) message(sprintf("%s called",method)) if (!quietly) cat(".Basis from attributes:\n") if (!quietly) print(attr(object,".Basis")) # NULL if (print_dims) { if (!quietly) print(dim(attr(object,".Basis"))) } return(attr(object,".Basis")) } assign(method,sim_generic,.GlobalEnv) invisible(NULL) } ## (**) these methods should (??) _mask_ package versions ... ## works in source(), not in devtools::test() ... mk_method("NULL") mk_method("numeric") mk_method("array",print_dims=TRUE) mk_method("") test_that("simple numerics", { ## expect_equal(simulate(1~.),1) ## FIXME re-enable if we resolve (**) above ## One-sided formula is not the same as an LHS that evaluates to NULL: expect_equal(simulate(NULL~.),NULL) }) test_that("raw formulas", { expect_error(suppressWarnings(simulate(x~.)), "Error evaluating") }) simulate.formula_lhs_character <- function(object, nsim=1, seed=NULL, ...) { if (!quietly) message("simulate.formula_lhs_character() called.") if (!quietly) print(ls(all.names=TRUE)) NextMethod() # Calls simulate.formula(), resulting in an infinite recursion. } test_that("prevent recursion", { expect_error(simulate("a"~.), "No applicable method") }) dd <- expand.grid(A=factor(1:3),B=factor(1:10),rep=1:10) test_that("two-sided formula warning", { expect_error(suppressMessages(simulate(.~1 + (A|B), newdata=dd, newparams=list(beta=1,theta=rep(1,6), sigma=1), family=gaussian, seed=101))[[1]], "object '.' not found") }) ## cleanup ## I can't figure out what environments these things actually live in so I'm going to ## give up and try() to remove them ... ## rmx <- function(s) if (exists(s, parent.frame())) rm(list=s, envir=parent.frame()) ## rmx("simulate.formula_lhs_character") ## rmx("simulate.formula_lhs_") ## rmx("simulate.formula_lhs_numeric") suppressWarnings(try(rm(list = c("simulate.formula_lhs_", "simulate.formula_lhs_numeric")),silent=TRUE)) lme4/tests/testthat/test-resids.R0000644000176200001440000000666615103764661016535 0ustar liggesusers#context("residuals") test_that("lmer", { C1 <- lmerControl(optimizer="nloptwrap", optCtrl=list(xtol_abs=1e-6, ftol_abs=1e-6)) fm1 <- lmer(Reaction ~ Days + (Days|Subject),sleepstudy, control=C1) fm2 <- lmer(Reaction ~ Days + (Days|Subject),sleepstudy, control=lmerControl(calc.derivs=FALSE, optimizer="nloptwrap", optCtrl=list(xtol_abs=1e-6, ftol_abs=1e-6))) expect_equal(resid(fm1), resid(fm2)) expect_equal(range(resid(fm1)), c(-101.17996, 132.54664), tolerance=1e-6) expect_equal(range(resid(fm1, scaled=TRUE)), c(-3.9536067, 5.1792598), tolerance=1e-6) expect_equal(resid(fm1,"response"),resid(fm1)) expect_equal(resid(fm1,"response"),resid(fm1,type="working")) expect_equal(resid(fm1,"deviance"),resid(fm1,type="pearson")) expect_equal(resid(fm1),resid(fm1,type="pearson")) ## because no weights given expect_error(residuals(fm1,"partial"), "partial residuals are not implemented yet") sleepstudyNA <- sleepstudy na_ind <- c(10,50) sleepstudyNA[na_ind,"Days"] <- NA fm1NA <- update(fm1,data=sleepstudyNA) fm1NA_exclude <- update(fm1,data=sleepstudyNA,na.action="na.exclude") expect_equal(length(resid(fm1)),length(resid(fm1NA_exclude))) expect_true(all(is.na(resid(fm1NA_exclude)[na_ind]))) expect_true(!any(is.na(resid(fm1NA_exclude)[-na_ind]))) }) test_that("glmer", { gm1 <- glmer(incidence/size ~ period + (1|herd), cbpp, family=binomial, weights=size) gm2 <- update(gm1,control=glmerControl(calc.derivs=FALSE)) gm1.old <- update(gm1,control=glmerControl(calc.derivs=FALSE, use.last.params=TRUE)) expect_equal(resid(gm1),resid(gm2)) ## y, wtres, mu change ?? ## FIX ME:: why does turning on derivative calculation make these tests fail??? expect_equal(range(resid(gm1.old)), c(-3.197512,2.356677), tolerance=1e-6) expect_equal(range(resid(gm1)), c(-3.1975034,2.35668826), tolerance=1e-6) expect_equal(range(resid(gm1.old, "response")), c(-0.1946736,0.3184579), tolerance=1e-6) expect_equal(range(resid(gm1,"response")),c(-0.194674747774946, 0.318458889275477)) expect_equal(range(resid(gm1.old, "pearson")), c(-2.381643,2.879069),tolerance=1e-5) expect_equal(range(resid(gm1,"pearson")), c(-2.38163599828335, 2.87908806084918)) expect_equal(range(resid(gm1.old, "working")), c(-1.241733,5.410587),tolerance=1e-5) expect_equal(range(resid(gm1, "working")), c(-1.24173431447365, 5.41064465283686)) expect_equal(resid(gm1),resid(gm1,scaled=TRUE)) ## since sigma==1 expect_error(resid(gm1,"partial"), "partial residuals are not implemented yet") cbppNA <- cbpp na_ind <- c(10,50) cbppNA[na_ind,"period"] <- NA gm1NA <- update(gm1,data=cbppNA) gm1NA_exclude <- update(gm1,data=cbppNA,na.action="na.exclude") expect_equal(length(resid(gm1)),length(resid(gm1NA_exclude))) expect_true(all(is.na(resid(gm1NA_exclude)[na_ind]))) expect_true(!any(is.na(resid(gm1NA_exclude)[-na_ind]))) }) test_that("floating-point issues -> NaN dev resids", { dat <- data.frame(x=1:5, n=100, id=1:5) res <- glmer(cbind(x,n-x) ~ 1 + (1 | id), data=dat, family=binomial, control = glmerControl(check.conv.singular = "ignore")) r <- residuals(res, type="deviance") expect_equal(unname(r[3]), 0) }) lme4/tests/testthat/test-reformulas-import.R0000644000176200001440000000072715103163201020702 0ustar liggesusers## these tests may fail if run a second time in the same session, because of rlang only-report-once-per-session setting f <- ~ 1 + (1|f) test_that("deprecation warnings from lme4 formula processing", { expect_warning(subbars(f), "has moved") expect_warning(nobars(f), "has moved") expect_warning(findbars(f), "has moved") expect_warning(mkReTrms(findbars(f), fr = data.frame(f = factor(1:10))), "has moved") expect_warning(expandDoubleVerts(f), "has moved") }) lme4/tests/testthat/test-formulaEval.R0000644000176200001440000002014115103764661017501 0ustar liggesusers#context("data= argument and formula evaluation") ## intercept context-dependent errors ... it's too bad that ## these errors differ between devtools::test() and ## R CMD check, but finding the difference is too much ## of a nightmare ## n.b. could break in other locales *if* we ever do internationalization ... data_RE <- "(bad 'data'|variable lengths differ)" test_that("glmerFormX", { set.seed(101) n <- 50 x <- rbinom(n, 1, 1/2) y <- rnorm(n) z <- rnorm(n) r <- sample(1:5, size=n, replace=TRUE) d <- data.frame(x,y,z,r) F <- "z" rF <- "(1|r)" modStr <- (paste("x ~", "y +", F, "+", rF)) modForm <- as.formula(modStr) ## WARNING: these drop/environment tests are extremely sensitive to environment ## they may fail/not fail, or fail differently, within a "testthat" environment vs. ## when run interactively expect_that(m_data.3 <- glmer( modStr , data=d, family="binomial"), is_a("glmerMod")) expect_that(m_data.4 <- glmer( "x ~ y + z + (1|r)" , data=d, family="binomial"), is_a("glmerMod")) ## interactively: (interactive() is TRUE {i.e. doesn't behave as I would expect} within testing environment ... ## if (interactive()) { ## AICvec <- c(77.0516381151634, 75.0819116367084, 75.1915023640827) ## expect_equal(drop1(m_data.3)$AIC,AICvec) ## expect_equal(drop1(m_data.4)$AIC,AICvec) ## } else { ## in test environment [NOT test_ expect_error(drop1(m_data.3),data_RE) expect_error(drop1(m_data.4),data_RE) ##} }) test_that("glmerForm", { set.seed(101) n <- 50 x <- rbinom(n, 1, 1/2) y <- rnorm(n) z <- rnorm(n) r <- sample(1:5, size=n, replace=TRUE) d <- data.frame(x,y,z,r) F <- "z" rF <- "(1|r)" modStr <- (paste("x ~", "y +", F, "+", rF)) modForm <- as.formula(modStr) ## formulas have environments associated, but character vectors don't ## data argument not specified: ## should work, but documentation warns against it expect_that(m_nodata.0 <- glmer( x ~ y + z + (1|r) , family="binomial"), is_a("glmerMod")) expect_that(m_nodata.1 <- glmer( as.formula(modStr) , family="binomial"), is_a("glmerMod")) expect_that(m_nodata.2 <- glmer( modForm , family="binomial"), is_a("glmerMod")) expect_that(m_nodata.3 <- glmer( modStr , family="binomial"), is_a("glmerMod")) expect_that(m_nodata.4 <- glmer( "x ~ y + z + (1|r)" , family="binomial"), is_a("glmerMod")) ## apply drop1 to all of these ... m_nodata_List <- list(m_nodata.0, m_nodata.1,m_nodata.2,m_nodata.3,m_nodata.4) d_nodata_List <- lapply(m_nodata_List,drop1) rm(list=c("x","y","z","r")) ## data argument specified expect_that(m_data.0 <- glmer( x ~ y + z + (1|r) , data=d, family="binomial"), is_a("glmerMod")) expect_that(m_data.1 <- glmer( as.formula(modStr) , data=d, family="binomial"), is_a("glmerMod")) expect_that(m_data.2 <- glmer( modForm , data=d, family="binomial"), is_a("glmerMod")) expect_that(m_data.3 <- glmer( modStr , data=d, family="binomial"), is_a("glmerMod")) expect_that(m_data.4 <- glmer( "x ~ y + z + (1|r)" , data=d, family="binomial"), is_a("glmerMod")) ff <- function() { set.seed(101) n <- 50 x <- rbinom(n, 1, 1/2) y <- rnorm(n) z <- rnorm(n) r <- sample(1:5, size=n, replace=TRUE) d2 <- data.frame(x,y,z,r) glmer( x ~ y + z + (1|r), data=d2, family="binomial") } m_data.5 <- ff() ff2 <- function() { set.seed(101) n <- 50 x <- rbinom(n, 1, 1/2) y <- rnorm(n) z <- rnorm(n) r <- sample(1:5, size=n, replace=TRUE) glmer( x ~ y + z + (1|r), family="binomial") } m_data.6 <- ff2() m_data_List <- list(m_data.0,m_data.1,m_data.2,m_data.3,m_data.4,m_data.5,m_data.6) badNums <- 4:5 d_data_List <- lapply(m_data_List[-badNums],drop1) ## these do NOT fail if there is a variable 'd' living in the global environment -- ## they DO fail in the testthat context expect_error(drop1(m_data.3),data_RE) expect_error(drop1(m_data.4),data_RE) ## expect_error(lapply(m_data_List[4],drop1)) ## expect_error(lapply(m_data_List[5],drop1)) ## d_data_List <- lapply(m_data_List,drop1,evalhack="parent") ## fails on element 1 ## d_data_List <- lapply(m_data_List,drop1,evalhack="formulaenv") ## fails on element 4 ## d_data_List <- lapply(m_data_List,drop1,evalhack="nulldata") ## succeeds ## drop1(m_data.5,evalhack="parent") ## 'd2' not found ## drop1(m_data.5,evalhack="nulldata") ## 'x' not found (d2 is in environment ...) ## should we try to make update smarter ... ?? ## test equivalence of (i vs i+1) for all models, all drop1() results for (i in 1:(length(m_nodata_List)-1)) { expect_equivalent(m_nodata_List[[i]],m_nodata_List[[i+1]]) expect_equivalent(d_nodata_List[[i]],d_nodata_List[[i+1]]) } expect_equivalent(m_nodata_List[[1]],m_data_List[[1]]) expect_equivalent(d_nodata_List[[1]],d_data_List[[1]]) for (i in 1:(length(m_data_List)-1)) { expect_equivalent(m_data_List[[i]],m_data_List[[i+1]]) } ## allow for dropped 'bad' vals for (i in 1:(length(d_data_List)-1)) { expect_equivalent(d_data_List[[i]],d_data_List[[i+1]]) } }) test_that("lmerForm", { set.seed(101) x <- rnorm(10) y <- rnorm(10) z <- rnorm(10) r <- sample(1:3, size=10, replace=TRUE) d <- data.frame(x,y,z,r) ## example from Joehanes Roeby m2 <- suppressWarnings(lmer(x ~ y + z + (1|r), data=d)) ff <- function() { m1 <- suppressWarnings(lmer(x ~ y + z + (1|r), data=d)) return(anova(m1)) } ff1 <- Reaction ~ Days + (Days|Subject) fm1 <- lmer(ff1, sleepstudy) fun <- function () { ff1 <- Reaction ~ Days + (Days|Subject) fm1 <- suppressWarnings(lmer(ff1, sleepstudy)) return (anova(fm1)) } anova(m2) ff() expect_equal(anova(m2),ff()) anova(fm1) fun() expect_equal(anova(fm1),fun()) ## test deparsing of long RE terms varChr <- paste0("varname_",outer(letters,letters,paste0)[1:100]) rvars <- varChr[1:9] form <- as.formula(paste("y ~",paste(varChr,collapse="+"), "+", paste0("(",paste(rvars,collapse="+"),"|f)"))) ff <- lme4:::reOnly(form) environment(ff) <- .GlobalEnv expect_equal(ff, ~(varname_aa + varname_ba + varname_ca + varname_da + varname_ea + varname_fa + varname_ga + varname_ha + varname_ia | f)) }) test_that("lapply etc.", { ## copied from dplyr failwith <- function (default = NULL, f, quiet = FALSE) { function(...) { out <- default try(out <- f(...), silent = quiet) out } } lmer_fw <- failwith(NULL,function(...) lmer(...) ,quiet=TRUE) expect_is(lmer_fw(Yield ~ 1|Batch, Dyestuff, REML = FALSE), "merMod") ## GH 369 listOfFormulas <- list( cbind(incidence, size - incidence) ~ 1 + (1 | herd), cbind(incidence, size - incidence) ~ period + (1 | herd)) expect_is(lapply(listOfFormulas,glmer,family=binomial,data=cbpp),"list") }) test_that("formula and data validation work with do.call() in artificial environment", { ## This ensures compatibility of lmer when it's called from the ## C-level Rf_eval() with an environment that doesn't exist on the ## stack (i.e. C implementation in magrittr 2.0) e <- new.env() e$. <- mtcars expect_is( do.call(lme4::lmer, list("disp ~ (1 | cyl)", quote(.)), envir = e), "merMod" ) fn <- function(data) { lme4::lmer("disp ~ (1 | cyl)", data = data) } expect_is( do.call(fn, list(quote(.)), envir = e), "merMod" ) }) test_that("correct environment on reOnly()", { ## GH 654 f <- Reaction ~ Days + (1 | Subject) e <- environment(f) m <- lmer(f, data = sleepstudy) expect_identical(environment(formula(m)), e) # TRUE expect_identical(environment(formula(m, fixed.only = TRUE)), e) # TRUE expect_identical(ee <- environment(formula(m, random.only = TRUE)), e) # FALSE }) lme4/tests/testthat/test-glmernbref.R0000644000176200001440000000117615103764661017356 0ustar liggesusers## DON'T load lme4; test is to see if glmer.nb works when ## lme4 is not loaded ## this does *not* work properly in a devtools::test environment ## (lme4 is not really detached) ## see tests/test-glmernbref.R for working test ... test_that("glmer.nb ref to glmer", { set.seed(101) dd <- data.frame(x=runif(200), f= rep(1:20, each=10)) b <- rnorm(20) dd <- transform(dd, y = rnbinom(200, mu = exp(1 + 2*x + b[f]), size = 2)) ## lme4 may not be attached if running tests via devtools::test() if ("package:lme4" %in% search()) detach("package:lme4") g <- lme4::glmer.nb(y~x + (1|f), data = dd) expect_is(g, "glmerMod") }) lme4/tests/testthat/test-eval.R0000644000176200001440000000451515103764661016162 0ustar liggesusers## examples for eval lookup testthat::skip_on_cran() data("cbpp", package = "lme4") if (require(car, quietly = TRUE)) { test_that("infIndexPlot env lookup OK", { fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) ## silly test; the point is to see if this errors out with ## Error in as.list.environment(X[[i]], ...) : ## promise already under evaluation: recursive default argument reference or earlier problems? ## Calls: infIndexPlot -> influence -> influence.merMod -> lapply -> FUN expect_equal(car::infIndexPlot(influence(fm1, "Subject")), NULL) }) } if (require(rr2, quietly = TRUE)) { test_that("rr2 env lookup OK", { ## Error under alternate eval lookup ## Error: bad 'data': object 'd' not found set.seed(123456) p1 <- 10; nsample <- 20; n <- p1 * nsample d <- data.frame(x1 = rnorm(n = n), x2 = rnorm(n = n), u1 = rep(1:p1, each = nsample), u2 = rep(1:p1, times = nsample)) d$u1 <- as.factor(d$u1); d$u2 <- as.factor(d$u2) ## LMM: y with random intercept b1 <- 1; b2 <- -1; sd1 <- 1.5 d$y_re_intercept <- b1 * d$x1 + b2 * d$x2 + rep(rnorm(n = p1, sd = sd1), each = nsample) + # random intercept u1 rep(rnorm(n = p1, sd = sd1), times = nsample) + # random intercept u2 rnorm(n = n) z.f2 <- lme4::lmer(y_re_intercept ~ x1 + x2 + (1 | u1) + (1 | u2), data = d, REML = T) ## NOTE, fails to produce warnings on second run of devtools::test() ## (possible interference from lmerTest methods being loaded ...?) expect_warning(R2(z.f2), "mod updated with REML = F") }) } ## semEff::VIF() is here being applied to a previously fitted lmer ## model (shipley.growth[[3]]) ## previous messing around with env evaluation had messed this up if (suppressWarnings(require(semEff))) { ## suppress warning about 'cov2cor' import replacement test_that("semEff env lookup OK", { ## Error in as.list.environment(X[[i]], ...) : ## promise already under evaluation: recursive default argument reference or earlier problems? ## Calls: VIF ... update.merMod -> do.call -> lapply -> FUN -> as.list.environment m <- shipley.growth[[3]] expect_equal(VIF(m), c(Date = 6.06283840168881, DD = 6.07741017455859, lat = 1.01215136160858) ) }) } lme4/tests/testthat/test-methods.R0000644000176200001440000011371315113136605016667 0ustar liggesusers(testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) ## use old (<=3.5.2) sample() algorithm if necessary if ("sample.kind" %in% names(formals(RNGkind))) suppressWarnings(RNGkind("Mersenne-Twister", "Inversion", "Rounding")) L <- load(system.file("testdata", "lme-tst-fits.rda", package="lme4", mustWork=TRUE)) ## FIXME: should test for old R versions, skip reloading test data in that ## case? fm0 <- fit_sleepstudy_0 fm1 <- fit_sleepstudy_1 fm2 <- fit_sleepstudy_2 gm1 <- fit_cbpp_1 gm2 <- fit_cbpp_2 gm3 <- fit_cbpp_3 ## More objects to use in all contexts : set.seed(101) dNA <- data.frame( xfac= sample(letters[1:10], 100, replace=TRUE), xcov= runif(100), resp= rnorm(100)) dNA[sample(1:100, 10), "xcov"] <- NA CI.boot <- function(fm, nsim=10, seed=101, ...) suppressWarnings(confint(fm, method="boot", nsim=nsim, quiet=TRUE, seed=seed, ...)) ## rSimple <- function(rep = 2, m.u = 2, kinds = c('fun', 'boring', 'meh')) { stopifnot(is.numeric(rep), rep >= 1, is.numeric(m.u), m.u >= 1, is.character(kinds), (nk <- length(kinds)) >= 1) nobs <- rep * m.u * nk data.frame(kind= rep(kinds, each=rep*m.u), unit = gl(m.u, 1, nobs), y = round(50*rnorm(nobs))) } d12 <- rSimple() data("Pixel", package="nlme") nPix <- nrow(Pixel) fmPix <- lmer(pixel ~ day + I(day^2) + (day | Dog) + (1 | Side/Dog), data = Pixel) test_that("summary", { ## test for multiple-correlation-warning bug and other 'correlation = *' variants ## Have 2 summary() versions, each with 3 print(.) ==> 6 x capture.output(.) sf.aa <- summary(fit_agridat_archbold) msg1 <- "Correlation.* not shown by default" ## message => *not* part of capture.*(.) expect_message(c1 <- capture.output(sf.aa), msg1) # correlation = NULL - default cF <- capture.output(print(sf.aa, correlation=FALSE)) ## TODO? ensure the above gives *no* message/warning/error expect_identical(c1, cF) expect_message( cT <- capture.output(print(sf.aa, correlation=TRUE)) , "Correlation.* could have been required in summary()") expect_identical(cF, cT[seq_along(cF)]) sfT.aa <- summary(fit_agridat_archbold, correlation=TRUE) ## no message any more ## expect_message(cT2 <- capture.output(sfT.aa), msg1) ## expect_identical(cF, cT2) cT3 <- capture.output(print(sfT.aa, correlation=TRUE)) expect_identical(cT, cT3) cF2 <- capture.output(print(sfT.aa, correlation=FALSE)) expect_identical(cF, cF2) }) test_that("lmer anova", { aa <- suppressMessages(anova(fm0,fm1)) expect_that(aa, is_a("anova")) expect_equal(names(aa), c("npar", "AIC", "BIC", "logLik", "-2*log(L)", "Chisq", "Df", "Pr(>Chisq)")) expect_warning(suppressMessages(do.call(anova,list(fm0,fm1))), "assigning generic names") ## dat <- data.frame(y = 1:5, u = c(rep("A",2), rep("B",3)), t = c(rep("A",3), rep("B",2))) datfun <- function(x) dat aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa <- dat expect_is(stats::anova(lmer(y ~ u + (1 | t), dat = aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa, REML=FALSE), lmer(y ~ 1 + (1 | t), dat = aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa, REML=FALSE)), "anova") expect_equal(rownames(stats::anova(lmer(y ~ u + (1 | t), dat = aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa, REML=FALSE), lmer(y ~ 1 + (1 | t), dat = aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa, REML=FALSE), model.names=c("a","b"))), c("b","a")) ff <- function(form) { lmer(form, dat=dat, REML=FALSE, control=lmerControl(check.conv.singular="ignore")) } expect_error(rownames(stats::anova(ff(y ~ u + (1 | t)), ff(y ~ 1 + (1 | t)), model.names=c("a","b","c"))), "different lengths") z <- 1 ## output not tested (but shouldn't fail) ss <- stats::anova(lmer(y ~ u + (1 | t), data = datfun(z), REML=FALSE), lmer(y ~ 1 + (1 | t), data = datfun(z), REML=FALSE)) ## ## from Roger Mundry via Roman Lustrik full <- lmer(resp ~ xcov + (1|xfac), data=dNA) null <- lmer(resp ~ 1 + (1|xfac), data=dNA) expect_error(anova(null,full), "models were not all fitted to the same size of dataset") }) if (requireNamespace("merDeriv")) { test_that("summary with merDeriv", { library(merDeriv) cc <- capture.output(print(summary(fm1))) expect_true(any(grepl("Correlation of Fixed Effects", cc))) ## WARNING, this will detach package but may not undo ## method loading ... detach("package:merDeriv") }) } ## Github issue #256 from Jonas Lindeløv -- issue is *not* specific for this dataset test_that("Two models with subset() within lmer()", { full3 <- lmer(y ~ kind + (1|unit), subset(d12, kind != 'boring'), REML=FALSE) null3 <- update(full3, .~. - kind) op <- options(warn = 2) # no warnings! ano3 <- anova(full3, null3)## issue #256: had warning in data != data[[1]] : ... o3 <- capture.output(ano3) # now prints with only one 'Data:' expect_equal(1, grep("^Data:", o3)) d12sub <- subset(d12, kind != 'boring') expect_is(full3s <- lmer(y ~ kind + (1|unit), d12sub, REML=FALSE), "lmerMod") expect_is(null3s <- update(full3s, .~. - kind), "lmerMod") expect_is(ano3s <- anova(full3s, null3s), "anova") expect_equal(ano3, ano3s, check.attributes=FALSE) options(op) }) test_that("anova() of glmer+glm models", { dat <<- data.frame(y = 1:5, u = c(rep("A",2), rep("B",3)), t = c(rep("A",3), rep("B",2))) cs <- glmerControl(check.conv.singular = "ignore") ## ignore singular fits gm1 <- glmer(y~(1|u), data=dat[1:4,], family=poisson, control = cs) gm0 <- glm(y~1, data=dat[1:4,], family=poisson) gm2 <- glmer(y~(1|u), data=dat[1:4,], family=poisson,nAGQ=2, control = cs) aa <- anova(gm1,gm0) expect_equal(aa[2,"Chisq"],0) expect_error(anova(gm2,gm0),"incommensurate") }) test_that("anova() of lmer+glm models", { dat2 <- dat set.seed(101) dat2$y <- rnorm(5) fm1 <- lmer(y~(1|u),data=dat2,REML=FALSE) fm0 <- lm(y~1,data=dat2) aa2 <- anova(fm1,fm0) expect_equal(aa2[2,"Chisq"],0) expect_warning(anova(fm1,type="III"),"additional arguments ignored") }) test_that("set p-values to NA for equivalent models: #583", { fm0B <- fm0 aa <- suppressMessages(anova(fm0B,fm0)) expect_true(all(is.na(aa[["Pr(>Chisq)"]]))) }) test_that("long names", { ## GH names(sleepstudy) <- c("Reaction", "Days", "Subject_xxxxxxxxxxxxxxxxxxxxxxxxxxx") fm1 <- lmer(Reaction ~ Days + (Days | Subject_xxxxxxxxxxxxxxxxxxxxxxxxxxx), sleepstudy) fm2 <- lmer(Reaction ~ Days + (Days || Subject_xxxxxxxxxxxxxxxxxxxxxxxxxxx), sleepstudy) expect_equal(length(attributes(suppressMessages(anova(fm1,fm2)))$heading),4) }) if (testLevel>1) { #context("bootMer confint()") set.seed(47) test_that("bootMer", { ## testing bug-fix for ordering of sd/cor components in sd/cor matrix with >2 rows ## FIXME: This model makes no sense [and CI.boot() fails for "nloptwrap"!] dd <- expand.grid(A=factor(1:3),B=factor(1:10),rep=1:10) dd$y <- suppressMessages(simulate(~1 + (A|B), newdata=dd, newparams=list(beta=1,theta=rep(1,6), sigma=1), family=gaussian, seed=101))[[1]] m1 <- lmer(y ~ 1 + (A|B), data=dd, control=lmerControl(calc.deriv=FALSE)) ci <- CI.boot(m1,seed=101) ci2 <- CI.boot(m1,seed=101) expect_equal(ci,ci2) ci_50 <- CI.boot(m1,level=0.5,seed=101) expect_true(all(ci_50[,"25 %"]>ci[,"2.5 %"])) expect_true(all(ci_50[,"75 %"]1 test_that("change in deviance name for anova", { cc <- suppressMessages(capture.output(anova(fm0,fm1))) expect_identical(sum(grepl("deviance", cc)), 0L) expect_identical(sum(grepl("-2*log(L)", cc, fixed = TRUE)), 1L) }) test_that("confint", { load(system.file("testdata", "gotway_hessianfly.rda", package = "lme4")) ## generated via: ## gotway_hessianfly_fit <- glmer(cbind(y, n-y) ~ gen + (1|block), ## data=gotway.hessianfly, family=binomial, ## control=glmerControl(check.nlev.gtreq.5="ignore")) ## gotway_hessianfly_prof <- profile(gotway_hessianfly_fit,which=1) ## save(list=ls(pattern="gotway"),file="gotway_hessianfly.rda") expect_equal(confint(gotway_hessianfly_prof)[1,1],0) ## FIXME: should add tests for {-1,1} bounds on correlations as well expect_equal(c(confint(fm1,method="Wald",parm="beta_")), c(232.301892,8.891041,270.508318,12.043531), tolerance=1e-5) ## Wald gives NA for theta values expect_true(all(is.na(confint(fm1,method="Wald",parm="theta_")))) ## check names ci1.p <- suppressWarnings(confint(fm1,quiet=TRUE)) ci1.w <- confint(fm1,method="Wald") ci1.b <- CI.boot(fm1, nsim=2) expect_equal(dimnames(ci1.p), list(c(".sig01", ".sigma", "(Intercept)", "Days"), c("2.5 %", "97.5 %"))) expect_equal(dimnames(ci1.p),dimnames(ci1.w)) expect_equal(dimnames(ci1.p),dimnames(ci1.b)) ci1.p.n <- suppressWarnings(confint(fm1, quiet=TRUE, signames=FALSE)) ci1.w.n <- confint(fm1, method="Wald", signames=FALSE) ci1.b.n <- CI.boot(fm1, nsim=2, signames=FALSE) expect_equal(dimnames(ci1.p.n), list(c("sd_(Intercept)|Subject", "sigma", "(Intercept)", "Days"), c("2.5 %", "97.5 %"))) expect_equal(dimnames(ci1.p.n),dimnames(ci1.w.n)) expect_equal(dimnames(ci1.p.n),dimnames(ci1.b.n)) }) test_that("monotonic profile but bad spline", { ## doesn't produce warnings on Solaris, or win-builder, or M1mac ... skip_on_os("windows") skip_on_os("solaris") skip_on_os("mac", arch = "aarch64") ## test case of slightly wonky (spline fit fails) but monotonic profiles: ## simfun <- function(J,n_j,g00,g10,g01,g11,sig2_0,sig01,sig2_1){ N <- sum(rep(n_j,J)) x <- rnorm(N) z <- rnorm(J) mu <- c(0,0) sig <- matrix(c(sig2_0,sig01,sig01,sig2_1),ncol=2) u <- MASS::mvrnorm(J,mu=mu,Sigma=sig) b_0j <- g00 + g01*z + u[,1] b_1j <- g10 + g11*z + u[,2] y <- rep(b_0j,each=n_j)+rep(b_1j,each=n_j)*x + rnorm(N,0,sqrt(0.5)) sim_data <- data.frame(Y=y,X=x,Z=rep(z,each=n_j), group=rep(1:J,each=n_j)) } set.seed(102) dat <- simfun(10,5,1,.3,.3,.3,(1/18),0,(1/18)) fit <- lmer(Y~X+Z+X:Z+(X||group),data=dat) expect_warning(pp <- profile(fit,"theta_"), "non-monotonic profile") expect_warning(cc <- confint(pp),"falling back to linear interpolation") ## very small/unstable problem, needs large tolerance expect_equal(unname(cc[2,]), c(0, 0.509), tolerance=0.09) # "bobyqa" had 0.54276 }) test_that("confint with bad profile", { badprof <- readRDS(system.file("testdata","badprof.rds", package="lme4")) expect_warning(cc <- confint(badprof), "falling back to linear") expect_equal(cc, array(c(0, -1, 2.50856219044636, 48.8305727797906, NA, NA, 33.1204478717389, 1, 7.33374326592662, 68.7254711217912, -6.90462047196017, NA), dim = c(6L, 2L), dimnames = list(c(".sig01", ".sig02", ".sig03", ".sigma", "(Intercept)", "cYear"), c("2.5 %", "97.5 %"))), tolerance=1e-3) }) test_that("refit", { s1 <- simulate(fm1) expect_is(refit(fm1,s1), "merMod") s2 <- simulate(fm1,2) expect_error(refit(fm1,s2), "refit not implemented .* lists") data(Orthodont,package = "nlme") fmOrth <- fm <- lmer(distance ~ I(age - 11) + (I(age - 11) | Subject), data = Orthodont) expect_equal(s1 <- simulate(fm,newdata = Orthodont,seed = 101), s2 <- simulate(fm,seed = 101)) ## works *without* offset ... m5 <- glmer(round(Reaction) ~ Days + (1|Subject), data = sleepstudy, family=poisson, offset=rep(0,nrow(sleepstudy))) m5R <- refit(m5) ## lots of fussy details make expect_equal() on the whole object difficult expect_equal(coef(m5),coef(m5R),tolerance=3e-6) expect_equal(VarCorr(m5),VarCorr(m5R),tolerance=1e-6) expect_equal(logLik(m5),logLik(m5R)) }) if (testLevel>1) { #context("predict method") test_that("predict", { ## when running via source(), cbpp has been corrupted at this point ## (replaced by a single empty factor obs() d1 <- expand.grid(period = unique(cbpp$period), herd = unique(cbpp$herd)) d2 <- data.frame(period = "1", herd = unique(cbpp$herd)) d3 <- expand.grid(period = as.character(1:3), herd = unique(cbpp$herd)) p0 <- predict(gm1) p1 <- predict(gm1,d1) p2 <- predict(gm1,d2) p3 <- predict(gm1,d3) expect_equal(p0[1], p1[1]) expect_equal(p0[1], p2[1]) expect_equal(p0[1], p3[1]) expect_error(predict(gm1, ReForm=NA)) ## matrix-valued predictors: Github #201 from Fabian S. sleepstudy$X <- cbind(1, sleepstudy$Days) m <- lmer(Reaction ~ -1 + X + (Days | Subject), sleepstudy) pm <- predict(m, newdata=sleepstudy) expect_is(pm, "numeric") expect_equal(quantile(pm, names = FALSE), c(211.0108, 260.9496, 296.873, 328.6378, 458.1584), tol=1e-5) op <- options(warn = 2) # there should be no warnings! if (require("MEMSS",quietly=TRUE)) { ## test spurious warning with factor as response variable data("Orthodont", package = "MEMSS") # (differently "coded" from the 'default' "nlme" one) silly <- glmer(Sex ~ distance + (1|Subject), data = Orthodont, family = binomial) sillypred <- data.frame(distance = c(20, 25)) ps <- predict(silly, sillypred, re.form=NA, type = "response") expect_is(ps, "numeric") expect_equal(unname(ps), c(0.999989632, 0.999997201), tolerance=1e-6) detach("package:MEMSS") } ## a case with interactions (failed in one temporary version): expect_warning(fmPixS <<- update(fmPix, .~. + Side), "nearly unidentifiable|unable to evaluate scaled gradient|failed to converge") ## (1|2|3); 2 and 3 seen (as Error??) on CRAN's Windows 32bit options(op) set.seed(1); ii <- sample(nrow(Pixel), 16) expect_equal(predict(fmPix, newdata = Pixel[ii,]), fitted(fmPix )[ii]) expect_equal(predict(fmPixS, newdata = Pixel[ii,]), fitted(fmPixS)[ii]) set.seed(7); n <- 100; y <- rnorm(n) dd <- data.frame(id = factor(sample(10, n, replace = TRUE)), x1 = 1, y = y, x2 = rnorm(n, mean = sign(y))) expect_message(m <- lmer(y ~ x1 + x2 + (1 | id), data = dd), "fixed-effect model matrix is rank deficient") expect_is(summary(m),"summary.merMod") ii <- sample(n, 16) expect_equal(predict(m, newdata = dd[ii,]), fitted(m)[ii]) ## predict(*, new..) gave Error in X %*% fixef(object) - now also drops col. ## predict(*, new..) with NA in data {and non-simple model}, issue #246: m1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) sleepst.NA <- sleepstudy ; sleepst.NA$Days[2] <- NA m2 <- update(fm1, data = sleepst.NA) ## maybe tricky for evaluation; fm1 was defined elsewhere, so data expect_equal(length(predict(m2, sleepst.NA[1:4,])),4) ## Wrong 'b' constructed in mkNewReTrms() -- issue #257 data(Orthodont,package="nlme") Orthodont <- within(Orthodont, nsex <- as.numeric(Sex == "Male")) m3 <- lmer(distance ~ age + (age|Subject) + (0 + Sex |Subject), data=Orthodont, control=lmerControl(check.conv.hess="ignore", check.conv.grad="ignore")) m4 <- lmer(distance ~ age + (age|Subject) + (0 + nsex|Subject), data=Orthodont) expect_equal(p3 <- predict(m3, Orthodont), fitted(m3), tolerance=1e-14) expect_equal(p4 <- predict(m4, Orthodont), fitted(m4), tolerance=1e-14) ## related to GH #275 (*passes*), ss <- sleepstudy set.seed(1) ss$noiseChar <- ifelse(runif(nrow(sleepstudy)) > 0.8, "Yes", "No") ss$noiseFactor <- factor(ss$noiseChar) fm4 <- lmer(Reaction ~ Days + noiseChar + (Days | Subject), ss) expect_equal(predict(fm4, newdata = model.frame(fm4)[2:3, ])[2], predict(fm4, newdata = model.frame(fm4)[3, ])) fm3 <- lmer(Reaction ~ Days + noiseFactor + (Days | Subject), ss) expect_equal(predict(fm3, newdata = model.frame(fm3)[2:3, ])[2], predict(fm3, newdata = model.frame(fm3)[3, ])) ## complex-basis functions in RANDOM effect fm5 <- lmer(Reaction~Days+(poly(Days,2)|Subject),sleepstudy) expect_equal(predict(fm5,sleepstudy[1,]),fitted(fm5)[1]) ## complex-basis functions in FIXED effect fm6 <- lmer(Reaction~poly(Days,2)+(1|Subject),sleepstudy) expect_equal(predict(fm6,sleepstudy[1,]),fitted(fm6)[1]) ## GH #414: no warning about dropping contrasts on random effects op <- options(warn = 2) # there should be no warnings! set.seed(1) dat <- data.frame( fac = factor(rep(c("a", "b"), 100)), grp = rep(1:25, each = 4)) dat$y <- 0 contr <- 0.5 * contr.sum(2) rownames(contr) <- c("a", "b") colnames(contr) <- "a" contrasts(dat$fac) <- contr m1_contr <- lmer(y~fac+(fac|grp),dat) pp <- predict(m1_contr,newdata=dat) options(op) }) ## testLevel>1 test_that("simulate", { ## simulate() will look for data in environment of formula, find ## unmodified version of cbpp -- need to re-add observation-level factor ee <- environment(formula(gm2)) ee$cbpp$obs <- factor(seq(nrow(ee$cbpp))) expect_is(simulate(gm2), "data.frame") p1 <- simulate(gm2, re.form = NULL, seed = 101) p2 <- simulate(gm2, re.form = ~0, seed = 101) p3 <- simulate(gm2, re.form = NA, seed = 101) p4 <- simulate(gm2, re.form = NULL, seed = 101) ## p5 was: sim with ReForm p6 <- simulate(gm2, re.form = NA, seed = 101) ## p7 was: sim with ReForm p8 <- simulate(gm2, re.form = ~0, seed = 101) p9 <- simulate(gm2, re.form = NA, seed = 101) p10 <- simulate(gm2,use.u = FALSE, seed = 101) p11 <- simulate(gm2,use.u = TRUE, seed = 101) ## minimal check of content: expect_identical(colSums(p1[,1]), c(incidence = 95, 747)) expect_identical(colSums(p2[,1]), c(incidence = 109, 733)) ## equivalences: ## group ~0: expect_equal(p2,p3) expect_equal(p2,p6) expect_equal(p2,p8) expect_equal(p2,p9) expect_equal(p2,p10) ## group 1: expect_equal(p1,p4) expect_equal(p1,p11) expect_error(simulate(gm2,use.u = TRUE, re.form = NA), "should specify only one") ## ## hack: test with three REs p1 <- lmer(diameter ~ (1|plate) + (1|plate) + (1|sample), Penicillin, control = lmerControl(check.conv.hess = "ignore", check.conv.grad = "ignore")) expect_is(sp1 <- simulate(p1, seed=123), "data.frame") expect_identical(dim(sp1), c(nrow(Penicillin), 1L)) expect_equal(fivenum(sp1[,1]), c(20.864, 22.587, 23.616, 24.756, 28.599), tolerance=0.01) ## Pixel example expect_identical(dim(simulate(fmPixS)), c(nPix, 1L)) expect_identical(dim(simulate(fmPix )), c(nPix, 1L)) ## simulation with newdata smaller/larger different from original fm <- lmer(diameter ~ 1 + (1|plate) + (1|sample), Penicillin) expect_is(simulate(fm,newdata=Penicillin[1:10,],allow.new.levels=TRUE),"data.frame") expect_is(simulate(fm,newdata=do.call(rbind,replicate(4,Penicillin,simplify=FALSE))),"data.frame") ## negative binomial sims set.seed(101) dd <- data.frame(f=factor(rep(1:10,each=20)), x=runif(200), y=rnbinom(200,size=2,mu=2)) g1 <- glmer.nb(y ~ x + (1|f), data=dd) th.g1 <- getME(g1, "glmer.nb.theta") ## changed to setting seed internally ts1 <- table(s1 <- simulate(g1,seed=101)[,1]) ## ts1B <- table(s1 <- simulate(g1,seed=101)[,1]) expect_equal(fixef(g1), c("(Intercept)" = 0.630067, x = -0.0167248), tolerance = 1e-4) ## ?? Travis is getting hung up here/ignoring tolerance spec?? expect_equal(th.g1, 2.013, tolerance = 1e-4) expect_equal(th.g1, g1@call$family[["theta"]])# <- important for pkg{effects} eval() expect_identical(sum(s1), 413) expect_identical(as.vector(ts1[as.character(0:5)]), ## c(51L, 54L, 36L, 21L, 14L, 9L)) c(49L,56L,32L,25L,11L,9L)) ## de novo NB simulation ... s2 <- simulate(~x + (1|f),seed=101, family=MASS::negative.binomial(theta=th.g1), newparams=getME(g1,c("theta","beta")), newdata=dd)[,1] expect_equal(s1,s2) ## Simulate with newdata with *new* RE levels: d <- sleepstudy[-1] # droping the response ("Reaction") ## d$Subject <- factor(rep(1:18, each=10)) ## Add 18 new subjects: d <- rbind(d, d) d$Subject <- factor(rep(1:36, each=10)) d$simulated <- simulate(fm1, seed=1, newdata = d, re.form=NULL, allow.new.levels = TRUE)[,1] expect_equal(mean(d$simulated), 299.9384608) ## Simulate with weights: newdata <- with(cbpp, expand.grid(period=unique(period), herd=unique(herd))) ss <- simulate(gm1, newdata=newdata[1:3,], weights=20, seed=101)[[1]] expect_equal(ss, matrix(c(4,2,0,16,18,20),nrow=3, dimnames=list(NULL,c("incidence","")))) ss <- simulate(gm3, newdata=newdata[1:3,], weights=20, seed=101)[[1]] expect_equal(ss,c(0.2,0.1,0.0)) ss <- simulate(gm1, newdata=newdata[1,], weights=20, seed=101)[[1]] expect_equal(unname(ss),matrix(c(4,16),nrow=1)) ## simulate Gamma, from function and de novo set.seed(102) dd <- data.frame(x=rep(seq(-2,2,length=15),10), f=factor(rep(1:10,each=15))) u <- rnorm(10) dd$y <- with(dd, rgamma(nrow(dd),shape=2, scale=exp(2+1*x+u[as.numeric(f)])/2)) g1 <- glmer(y~x+(1|f),family=Gamma(link="log"),dd) s1 <- simulate(g1,seed=101) s2 <- suppressMessages(simulate(~x+(1|f), family=Gamma(link="log"), seed=101, newdata=dd, newparams=getME(g1,c("theta","beta","sigma")))) expect_equal(s1, s2) dd$y2 <- s2[[1]] g2 <- glmer(y2~x+(1|f), family=Gamma(link="log"),dd) expect_equal(fixef(g2), tolerance = 4e-7, # 32-bit windows showed 1.34e-7 c(`(Intercept)` = 2.90871404438183, x = 0.988265230798941)) ## c("(Intercept)" = 2.81887136759369, x= 1.06543222163626)) ## simulate with re.form = NULL and derived/offset components in formula fm7 <- lmer(Reaction ~ Days + offset(Days) + (1|Subject), sleepstudy) s7 <- simulate(fm7, seed = 101, re.form = NULL) ## thought this would break but it doesn't ??? f_wrap <- function() { Reaction ~ Days + offset(Days) + (1|Subject) } fm8 <- lmer(f_wrap(), sleepstudy) s8 <- simulate(fm8, seed = 101, re.form = NULL) expect_identical(s7, s8) ## harder: insert NA values in the offset and see if it handles this OK?? }) #context("misc") test_that("misc", { expect_equal(df.residual(fm1),176) if (suppressWarnings(require(ggplot2))) { ## ggplot calls sample() [for silly start-up messages ## throws warning because we're using backward-compatible RNGkind expect_is(fortify.merMod(fm1), "data.frame") expect_is(fortify.merMod(gm1), "data.frame") } expect_is(as.data.frame(VarCorr(fm1)), "data.frame") }) } ## testLevel>1 #context("plot") test_that("plot", { ## test getData() within plot function: reported by Dieter Menne doFit <- function(){ data(Orthodont,package = "nlme") data1 <- Orthodont lmer(distance ~ age + (age|Subject), data = data1) } data(Orthodont, package = "nlme") fm0 <- lmer(distance ~ age + (age|Subject), data = Orthodont) expect_is(plot(fm0), "trellis") suppressWarnings(rm("Orthodont")) fm <- doFit() pp <- plot(fm, resid(., scaled = TRUE) ~ fitted(.) | Sex, abline = 0) expect_is(pp, "trellis") ## test qqmath/getIDLabels() expect_is(q1 <- lattice::qqmath(fm,id=0.05),"trellis") cake2 <- transform(cake,replicate=as.numeric(replicate), recipe=as.numeric(recipe)) fm2 <- lmer(angle ~ recipe + temp + (1|recipe:replicate), cake2, REML= FALSE) expect_is(lattice::qqmath(fm2, id=0.05), "trellis") expect_is(lattice::qqmath(fm2, id=0.05, idLabels=~recipe), "trellis") expect_warning(lattice::qqmath(fm2, 0.05, ~recipe), "please specify") expect_warning(lattice::qqmath(fm2, 0.05), "please specify") }) #context("misc") test_that("summary", { ## test that family() works when $family element is weird ## FIXME: is convergence warning here a false positive? gnb <- suppressWarnings(glmer(TICKS~1+(1|BROOD), family=MASS::negative.binomial(theta=2), data=grouseticks)) expect_is(family(gnb),"family") }) if (testLevel>1) { #context("profile") test_that("profile", { ## FIXME: can we deal with convergence warning messages here ... ? ## fit profile on default sd/cor scale ... p1 <- suppressWarnings(profile(fm1,which="theta_")) ## and now on var/cov scale ... p2 <- suppressWarnings(profile(fm1,which="theta_", prof.scale="varcov")) ## because there are no correlations, squaring the sd results ## gives the same result as profiling on the variance scale ## in the first place expect_equal(confint(p1)^2,confint(p2), tolerance=1e-5) ## or via built-in varianceProf() function expect_equal(unname(confint(varianceProf(p1))), unname(confint(p2)), tolerance=1e-5) p3 <- profile(fm2,which=c(1,3,4)) p4 <- suppressWarnings(profile(fm2,which="theta_",prof.scale="varcov", signames=FALSE)) ## compare only for sd/var components, not corr component ## FAILS on r-patched-solaris-x86 2018-03-30 ??? ## 2/6 mismatches (average diff: 4.62) ## [1] 207 - 216 == -9.23697 ## [4] 1422 - 1422 == -0.00301 if (Sys.info()["sysname"] != "SunOS") { expect_equal(unname(confint(p3)^2), unname(confint(p4)[c(1,3,4),]), tolerance=1e-3) } ## check naming convention properly adjusted expect_equal(as.character(unique(p4$.par)), c("var_(Intercept)|Subject", "cov_Days.(Intercept)|Subject", "var_Days|Subject", "sigma")) }) test_that("densityplot is robust", { p <- readRDS(system.file("testdata","harmel_profile.rds", package="lme4")) expect_warning(lattice::densityplot(p), "unreliable profiles for some variables") }) } ## testLevel>1 #context("model.frame") test_that("model.frame", { ## non-syntactic names d <- sleepstudy names(d)[1] <- "Reaction Time" ee <- function(m,nm) { expect_equal(names(model.frame(m, fixed.only=TRUE)),nm) } m <- lmer(Reaction ~ 1 + (1 | Subject), sleepstudy) ee(m,"Reaction") m2 <- lmer(Reaction ~ Days + (1 | Subject), sleepstudy) ee(m2,c("Reaction","Days")) m3 <- lmer(`Reaction Time` ~ Days + (1 | Subject), d) ee(m3, c("Reaction Time","Days")) m4 <- lmer(Reaction ~ log(1+Days) + (1 | Subject), sleepstudy) ee(m4, c("Reaction","log(1 + Days)")) }) #context("influence measures") d <- as.data.frame(ChickWeight) colnames(d) <- c("y", "x", "subj", "tx") dNAs <- d dNAs$y[c(1, 3, 5)] <- NA fitNAs <- lmer(y ~ tx*x + (x | subj), data = dNAs, na.action=na.exclude) test_that("influence/hatvalues works", { ifm1 <- influence(fm1, do.coef=FALSE) expect_equal(unname(head(ifm1$hat)), c(0.107483311203734, 0.102096105816528, 0.0980557017761242, 0.0953620990825215, 0.0940152977357202, 0.0940152977357202), tolerance=1e-6) expect_equal(nrow(dNAs),length(hatvalues(fitNAs))) }) test_that("influence OK with tibbles", { if (requireNamespace("tibble")) { ## make small data set/example so influence() isn't too slow ... ss <- tibble::as_tibble(sleepstudy[1:60,]) smallfit <- lmer(Reaction ~ 1 + (1 | Subject), data = ss) i1 <- influence(smallfit, ncores = 1) expect_equal(head(i1[["fixed.effects[-case]"]]), structure(c(286.35044481665, 286.179896199062, 286.327301507498, 285.014692121823, 284.36060419176, 283.297551183126), dim = c(6L, 1L), dimnames = list(c("1", "2", "3", "4", "5", "6"), "(Intercept)")), tolerance = 1e-6) } }) test_that("rstudent", { rfm1 <- rstudent(fm1) expect_equal(unname(head(rfm1)), c(-1.45598270922089, -1.49664543508657, -2.11747425025103, -0.0729690066951975, 0.772716397142335, 2.37859408861768), tolerance=1e-6) expect_equal(nrow(dNAs),length(rstudent(fitNAs))) }) test_that("cooks distance", { expect_equal( unname(head(cooks.distance(fm1))), c(0.127645976734753, 0.127346548123793, 0.243724627125036, 0.000280638917214881, 0.0309804642689636, 0.293554225380831), tolerance=1e-6) expect_equal(nrow(dNAs),length(cooks.distance(fitNAs))) }) test_that("cooks distance on subject-level influence", { ifm1S <- influence(fm1, "Subject", ncores=1) expect_equal( unname(head(cooks.distance(ifm1S),2)), c(0.33921460279262, 0.290309061006305), tolerance = 1e-6) }) test_that("cooks distance on glmer models", { inf <- influence(gm1) inf.h <- influence(gm1, "herd", ncores=1) cook <- cooks.distance(inf) expect_equal(unname(head(cook, 3)), c(0.0532998800033037, 0.0405931172763581, 0.252608337928438), tolerance = 1e-6) cook.h <- cooks.distance(inf.h) expect_equal(unname(head(cook.h, 3)), c(0.256630560723611, 0.00525856231971531, 0.103355658099396), tolerance = 1e-6) }) ## tweaked example so estimated var = 0 zerodat <- data.frame(x=seq(0,1,length.out=120), f=rep(1:3,each=40)) zerodat$y1 <- simulate(~x+(1|f), family=gaussian, seed=102, newparams=list(beta=c(1,1), theta=c(0.001), sigma=1), newdata=zerodat)[[1]] zerodat$y2 <- simulate(~x+(1|f), family=poisson, seed=102, newparams=list(beta=c(1,1), theta=c(0.001)), newdata=zerodat)[[1]] test_that("rstudent matches for zero-var cases", { lmer_zero <- lmer(y1~x+(1|f), data=zerodat) glmer_zero <- glmer(y2~x+(1|f),family=poisson, data=zerodat) lm_zero <- lm(y1~x, data=zerodat) glm_zero <- glm(y2~x,family=poisson, data=zerodat) expect_equal(suppressWarnings(rstudent(glmer_zero)), rstudent(glm_zero), tolerance=0.01) expect_equal(suppressWarnings(rstudent(lmer_zero)), rstudent(lm_zero),tolerance=0.01) }) if (testLevel>1) { ## n.b. influence() doesn't work under system.time(); ## weird evaluation stuff ? ## FIXME: work on timing some more i1 <- influence(fm1, ncores=1) test_that("full version of influence", { expect_equal(c(head(i1[["fixed.effects[-case]"]],1)), c(252.323536264131, 10.3222704729148)) }) cd <- cooks.distance(i1) expect_equal(unname(head(cd,2)), c(0.016503344184025, 0.0106634053477361)) if (parallel::detectCores() > 1) { test_that("parallel influence", { i2 <- suppressMessages(influence(fm1, ncores=2)) ## if (packageVersion("Matrix") != "1.4.2") ## fow now,as they differ str(i1) str(i2) print(all.equal(i1, i2)) # to see diff print(identical(i1, i2)) # expect_equal(i1, i2) ## <<<<-------------- FAILS (4 MM) }) } } ## car method testing: influence timing with ncores > 1 ... ## car version 3.0.10. ## L <- load(system.file("testdata", "lme-tst-fits.rda", ## package="lme4", mustWork=TRUE)) ## data("sleepstudy", package="lme4") ## library(lme4) ## library(car) ## WANT warning about S3 method overwrite ... ## fm1 <- fit_sleepstudy_1 ## library(pracma) ## because system.time() is weird ## tic(); i1 <- influence(fm1); toc() ## 2+ seconds ## tic(); i2 <- influence(fm1, ncores=8); toc() ## 3.4 seconds test_that("influence with nAGQ=0", { gm1Q0 <- update(gm1, nAGQ=0) expect_is(influence(gm1Q0), "influence.merMod") }) if (testLevel > 1) withAutoprint({ test_that("cook's distance comparison", { ## generate data with zero variance set.seed(101) n <- 50 dd <- data.frame(x = rnorm(n), f = factor(rep(1:2, each = n/2))) suppressMessages(dd$y <- simulate(~ x + (1|f), newdata = dd, newparams = list(beta = c(2,2), theta = 0, sigma = 5), family = gaussian)[[1]]) (fm2 <- lmer(y~x + (1|f), dd, REML = FALSE)) (fm2L <- lm(y~x , dd)) i2 <- influence(fm2) ## hatvalues version does **not** match exactly ... expect_equal(cooks.distance(i2), cooks.distance(fm2L)) expect_equal(cooks.distance(fm2), cooks.distance(fm2L), tolerance = 1e-2) }) }) ## testLevel > 1 test_that("oldNames warning in confint", { fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) expect_warning(confint(fm1, oldNames = TRUE)) }) lme4/tests/testthat/test-lmList.R0000644000176200001440000001730515103764661016500 0ustar liggesusers## use old (<=3.5.2) sample() algorithm if necessary if ("sample.kind" %in% names(formals(RNGkind))) { suppressWarnings(RNGkind("Mersenne-Twister", "Inversion", "Rounding")) } data("cbpp", package = "lme4") #context("lmList") test_that("basic lmList", { set.seed(17) fm1. <- lmList(Reaction ~ Days | Subject, sleepstudy, pool=FALSE) fm1 <- lmList(Reaction ~ Days | Subject, sleepstudy) cf.fm1 <- data.frame( `(Intercept)` = c(244.19267, 205.05495, 203.48423, 289.68509, 285.73897, 264.25161, 275.01911, 240.16291, 263.03469, 290.10413, 215.11177, 225.8346, 261.14701, 276.37207, 254.96815, 210.44909, 253.63604, 267.0448), Days = c(21.764702, 2.2617855, 6.1148988, 3.0080727, 5.2660188, 9.5667679, 9.1420455, 12.253141, -2.8810339, 19.025974, 13.493933, 19.504017, 6.4334976, 13.566549, 11.348109, 18.056151, 9.1884448, 11.298073)) expect_equal(signif(coef(fm1), 8), cf.fm1, tolerance = 1e-7, check.attributes=FALSE) expect_equal(coef(fm1.), coef(fm1)) expect_true(inherits(formula(fm1), "formula")) ## <- had been wrong till 2015-04-09 sm1. <- summary(fm1.) sm1 <- summary(fm1) expect_equal(sm1$RSE, 25.5918156267, tolerance = 1e-10) cf1 <- confint(fm1) ## Calling the plot.lmList4.confint() method : expect_true(inherits(pcf1 <- plot(cf1), "trellis")) }) test_that("orthodont", { data(Orthodont, package="nlme") fm2 <- lmList(distance ~ age | Subject, Orthodont) fe2 <- fixef(fm2) expect_equal(fe2, c("(Intercept)" = 16.7611111111111, age = 0.660185185185185)) expect_true(inherits(pairs(fm2), "trellis")) }) test_that("simulated", { set.seed(12) d <- data.frame( g = sample(c("A","B","C","D","E"), 250, replace=TRUE), y1 = runif(250, max=100), y2 = sample(c(0,1), 250, replace=TRUE) ) fm3.1 <- lmList(y1 ~ 1 | g, data=d) expect_equal(coef(fm3.1), structure(list(`(Intercept)` = c(45.8945525606396, 50.1127995110841, 49.5320538515225, 52.4286874305165, 48.7716343882989)), .Names = "(Intercept)", row.names = c("A", "B", "C", "D", "E"), class = "data.frame", label = "Coefficients", effectNames = "(Intercept)", standardized = FALSE)) cf31 <- confint(fm3.1) expect_true(inherits(plot(cf31), "trellis")) fm3.2 <- lmList(y2 ~ 1 | g, data=d, family=binomial) ## ^^^^^^^^ "glmList" cf32 <- suppressMessages(confint(fm3.2,quiet=TRUE)) expect_identical(dim(cf32), c(5L,2:1)) expect_true(inherits(plot(cf32), "trellis")) expect_equal(unname(getDataPart(signif(drop(cf32), 6))), cbind(c(-0.400041, -0.311489, -1.07774, -0.841075, -0.273828), c( 0.743188, 0.768538, 0.0723138, 0.274392, 0.890795))) }) test_that("cbpp", { ## "glmList" (2) -- here, herd == 8 has only one observation => not estimable expect_warning(fm4 <- lmList(cbind(incidence, size - incidence) ~ period | herd, family=binomial, data=cbpp), "Fitting failed for ") cf4 <- coef(fm4) # with some 5 NA's ## match NA locations expect_equal(dim(cf4),c(15,4)) expect_identical(which(is.na(cf4)), sort(as.integer(c(8+15*(0:3), 47)))) expect_warning(fm4B <- lme4::lmList(incidence ~ period | herd, data=cbpp), "Fitting failed") if(FALSE) { ## FIXME: this is actually an nlme bug ... ## https://bugs.r-project.org/bugzilla/show_bug.cgi?id=16542 try(summary(fm4)) ## Error in `[<-`(`*tmp*`, use, use, ii, value = lst[[ii]]) : ## subscript out of bounds library(nlme) data("cbpp",package="lme4") fm6 <- nlme::lmList(incidence ~ period | herd, data=cbpp) try(coef(fm6)) ## coef does *not* work here try(summary(fm6)) ## this is a slightly odd example because the residual df from ## these fits are in fact zero ... so pooled.SD fails, as it should } }) test_that("NA,weights,offsets", { ## from GH #320 set.seed(101) x <- 1:8 y <- c(2,2,5,4,3,1,2,1) g <- c(1,1,1,2,2,3,3,3) dat <- data.frame(x=x, y=y, g=g) m1 <- lmList(y ~ x | g, data=dat) expect_false(any(is.na(coef(m1)))) w <- runif(nrow(sleepstudy)) m2 <- lmList(Reaction ~ Days | Subject, weights=w, sleepstudy) ss <- subset(sleepstudy,Subject==levels(Subject)[1]) m2X <- lm(Reaction ~ Days, ss, weights=w[1:nrow(ss)]) expect_equal(coef(m2X),as.matrix(coef(m2))[1,]) m3 <- lmList(Reaction ~ Days | Subject, sleepstudy) m4 <- lmList(Reaction ~ Days | Subject, offset=w, sleepstudy) m4X <- lm(Reaction ~ Days, ss, offset=w[1:nrow(ss)]) expect_equal(coef(m4X),as.matrix(coef(m4))[1,]) expect_false(identical(m2,m3)) expect_false(identical(m4,m3)) m5 <- lmList(Reaction ~ Days + offset(w) | Subject, sleepstudy) expect_equal(coef(m5),coef(m4)) ## more from GH 320 dat2 <- data.frame(dat,xx=c(NA,NA,NA,1:4,NA)) m5 <- lmList(y ~ x | g, data=dat2) expect_equal(unlist(coef(m5)[1,]), coef(lm(y~x,subset=(g==1)))) expect_equal(unlist(coef(m5)[3,]), coef(lm(y~x,subset=(g==3)))) }) test_that("pooled", { ## GH #26 fm_lme4 <- lme4:::lmList(Reaction ~ Days | Subject, sleepstudy) fm_nlme <- nlme:::lmList(Reaction ~ Days | Subject, sleepstudy) fm_nlme_nopool <- nlme:::lmList(Reaction ~ Days | Subject, sleepstudy, pool=FALSE) ci_lme4_pooled <- confint(fm_lme4,pool=TRUE) #get low and high CI estimates and pooled sd ci_nlme_pooled <- nlme:::intervals(fm_nlme,pool=TRUE) expect_equal(unname(ci_lme4_pooled[,,1]),unname(ci_nlme_pooled[,c(1,3),1])) ci_lme4_nopool1 <- confint(fm_lme4,pool=FALSE) ci_lme4_nopool2 <- confint(fm_lme4) expect_identical(ci_lme4_nopool1,ci_lme4_nopool2) ## BUG in nlme::intervals ... ? can't get CIs on unpooled fits ## nlme::intervals(fm_nlme,pool=FALSE) ## nlme::intervals(fm_nlme_nopool) expect_equal(ci_lme4_nopool1[1:3,,1], structure(c(179.433862895996, 193.026448122379, 186.785722998616, 308.951475285822, 217.083442786712, 220.182727910474), .Dim = c(3L, 2L), .Dimnames = list(c("308", "309", "310"), c("2.5 %", "97.5 %")))) }) test_that("derived variables", { fm_lme4 <- lme4:::lmList(log(Reaction) ~ Days | Subject, sleepstudy) fm_nlme <- nlme:::lmList(log(Reaction) ~ Days | Subject, sleepstudy) expect_equal(c(coef(fm_lme4)),c(coef(fm_nlme)),tolerance=1e-5) }) test_that("subset", { data(MathAchieve, package="nlme") data(MathAchSchool, package="nlme") RB <- merge(MathAchieve, MathAchSchool[, c("School", "Sector")], by="School") names(RB) <- tolower(names(RB)) RB$cses <- with(RB, ses - meanses) cat.list.nlme <- nlme::lmList(mathach ~ cses | school, subset = sector=="Catholic", data=RB) cat.list.lme4 <- lme4::lmList(mathach ~ cses | school, subset = sector=="Catholic", data=RB) expect_equal(c(coef(cat.list.lme4)), c(coef(cat.list.nlme)),tolerance=1e-5) }) if (requireNamespace("tibble")) { test_that("avoid tibble warnings", { ## GH 645 op <- options(warn = 2) m1 <- lmList(Reaction ~ Days | Subject, data = sleepstudy) m2 <- lmList(Reaction ~ Days | Subject, data = tibble::as_tibble(sleepstudy)) expect_identical(coef(m1), coef(m2)) options(op) }) } lme4/tests/testthat/test-nbinom.R0000644000176200001440000001036015103764661016510 0ustar liggesuserstestLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 set.seed(101) dd <- expand.grid(f1 = factor(1:3), f2 = LETTERS[1:2], g=1:9, rep=1:15, KEEP.OUT.ATTRS=FALSE) mu <- 5*(-4 + with(dd, as.integer(f1) + 4*as.numeric(f2))) dd$y <- rnbinom(nrow(dd), mu = mu, size = 0.5) ## mimic glmer.nb protocol if (testLevel>1) { test_that("most messages suppressed", { expect_message(glmer.nb(y ~ f1 + (1|g), data=dd[1:10,]), "singular") }) test_that("ok with negative.binomial masking", { negative.binomial <- function() {} ## use shortened version of data for speed ... m.base <- glmer.nb(y ~ f1 + (1|g), data=dd[1:200,]) expect_is(m.base,"merMod") }) test_that("ok with Poisson masking", { poisson <- NA ## use shortened version of data for speed ... m.base <- glmer.nb(y ~ f1 + (1|g), data=dd[1:200,]) expect_is(m.base,"merMod") rm(poisson) }) if (testLevel>2) { #context("testing glmer refit") test_that("glmer refit", { ## basic Poisson fit m.base <- glmer(y ~ f1*f2 + (1|g), data=dd, family=poisson) expect_equal(m.base@beta,(m.base.r <- refit(m.base))@beta, tolerance = 1e-5) th <- lme4:::est_theta(m.base,limit=20,eps=1e-4,trace=FALSE) th0 <- structure(0.482681268108477, SE = 0.0244825021248148) th1 <- structure(0.482681277470945) th2 <- 0.482681268108477 th3 <- 0.4826813 ## NB update with raw number m.numth1 <- update(m.base,family=MASS::negative.binomial(theta=0.4826813)) expect_equal(m.numth1@beta,(m.numth1.r <- refit(m.numth1))@beta) ## strip NB value m.symth4 <- update(m.base,family=MASS::negative.binomial(theta=c(th))) expect_equal(m.symth4@beta,(m.symth4.r <- refit(m.symth4))@beta) ## IDENTICAL numeric value to case #1 above m.symth6 <- update(m.base,family=MASS::negative.binomial(theta=th3)) expect_equal(m.symth6@beta,(m.symth6.r <- refit(m.symth6))@beta) ## standard NB update with computed theta from est_theta (incl SE attribute) m.symth <- update(m.base,family=MASS::negative.binomial(theta=th)) expect_equal(m.symth@beta,(m.symth.r <- refit(m.symth))@beta) ## NB update with equivalent value m.symth2 <- update(m.base,family=MASS::negative.binomial(theta=th0)) expect_equal(m.symth2@beta,(m.symth2.r <- refit(m.symth2))@beta) ## NB update with theta value (stored as variable, no SE) only m.symth3 <- update(m.base,family=MASS::negative.binomial(theta=th1)) expect_equal(m.symth3@beta,(m.symth3.r <- refit(m.symth3))@beta) ## strip NB value (off by 5e-16) m.symth5 <- update(m.base,family=MASS::negative.binomial(theta=th2)) expect_equal(m.symth5@beta,(m.symth5.r <- refit(m.symth5))@beta) }) ## GH #399 test_that("na_exclude", { dd1 <- dd[1:200,] dd1$f1[1:5] <- NA expect_error(glmer.nb(y ~ f1 + (1|g), data=dd1, na.action=na.fail), "missing values in object") m1 <- glmer.nb(y ~ f1 + (1|g), data=dd1, na.action=na.omit) m2 <- glmer.nb(y ~ f1 + (1|g), data=dd1, na.action=na.exclude) expect_equal(fixef(m1),fixef(m1)) expect_equal(length(predict(m2))-length(predict(m1)),5) }) ## GH 423 test_that("start_vals", { dd1 <- dd[1:200,] g1 <- glmer.nb(y ~ f1 + (1|g), data=dd1) g2 <- glmer.nb(y ~ f1 + (1|g), data=dd1, initCtrl=list(theta=getME(g1,"glmer.nb.theta"))) expect_equal(fixef(g1),fixef(g2),tol=1e-5) }) test_that("control arguments", { dd1 <- dd[1:200,] g1 <- glmer.nb(y ~ f1 + (1|g), data=dd1, initCtrl=list(theta=10)) expect_is(g1,"merMod") ## dumb test - just checking for run w/o error suppressWarnings(g1 <- glmer.nb(y ~ f1 + (1|g), data=dd1, nb.control=glmerControl(optimizer="bobyqa"))) expect_equal(g1@optinfo$optimizer, "bobyqa") suppressWarnings(g1 <- glmer.nb(y ~ f1 + (1|g), data=dd1, nb.control=glmerControl(optCtrl=list(maxfun=2)))) expect_equal(g1@optinfo$feval,3) }) } ## testLevel>2 } ## testLevel>1 lme4/tests/testthat/test-glmFamily.R0000644000176200001440000001623115103764661017152 0ustar liggesuserseps <- .Machine$double.eps oneMeps <- 1 - eps set.seed(1) ## sample linear predictor values for the unconstrained families etas <- list( seq.int(-8, 8, by=1), runif(17, -8, 8), # random sample from wide uniform dist rnorm(17, 0, 8), # random sample from wide normal dist c(-10^30, rnorm(15, 0, 4), 10^30) ) ## sample linear predictor values for the families in which eta must be positive etapos <- list(seq.int(1, 20, by=1), rexp(20), rgamma(20, 3), pmax(.Machine$double.eps, rnorm(20, 2, 1))) ## values of mu in the (0,1) interval mubinom <- list(runif(100, 0, 1), rbeta(100, 1, 3), pmin(pmax(eps, rbeta(100, 0.1, 3)), oneMeps), pmin(pmax(eps, rbeta(100, 3, 0.1)), oneMeps)) test_that("inverse link and muEta functions", { tst.lnki <- function(fam, frm) { ff <- glmFamily$new(family=fam) ## as.numeric() needed for binomial()$linkinv breakage (also in muEta test) sapply(frm, function(x) expect_that(fam$linkinv(as.numeric(x)), equals(ff$linkInv(x)))) } tst.muEta <- function(fam, frm) { ff <- glmFamily$new(family=fam) sapply(frm, function(x) expect_that(fam$mu.eta(as.numeric(x)), equals(ff$muEta(x)))) } tst.lnki(binomial(), etas) # binomial with logit link tst.muEta(binomial(), etas) tst.lnki(binomial("probit"), etas) # binomial with probit link tst.muEta(binomial("probit"), etas) tst.lnki(binomial("cloglog"), etas) # binomial with cloglog link tst.muEta(binomial("cloglog"), etas) tst.lnki(binomial("cauchit"), etas) # binomial with cauchit link tst.muEta(binomial("cauchit"), etas) tst.lnki(poisson(), etas) # Poisson with log link tst.muEta(poisson(), etas) tst.lnki(gaussian(), etas) # Gaussian with identity link tst.muEta(gaussian(), etas) tst.lnki(Gamma(), etapos) # gamma family tst.muEta(Gamma(), etapos) tst.lnki(inverse.gaussian(), etapos) # inverse Gaussian tst.muEta(inverse.gaussian(), etapos) }) test_that("link and variance functions", { tst.link <- function(fam, frm) { ff <- glmFamily$new(family=fam) sapply(frm, function(x) expect_that(fam$linkfun(x), equals(ff$link(x)))) } tst.variance <- function(fam, frm) { ff <- glmFamily$new(family=fam) sapply(frm, function(x) expect_that(fam$variance(x), equals(ff$variance(x)))) } tst.link( binomial(), mubinom) tst.variance(binomial(), mubinom) tst.link( binomial("probit"), mubinom) tst.link( binomial("cauchit"), mubinom) tst.link( gaussian(), etas) tst.variance(gaussian(), etas) tst.link( Gamma(), etapos) tst.variance(Gamma(), etapos) tst.link( inverse.gaussian(), etapos) tst.variance(inverse.gaussian(), etapos) tst.variance(MASS::negative.binomial(1), etapos) tst.variance(MASS::negative.binomial(0.5), etapos) tst.link( poisson(), etapos) tst.variance(poisson(), etapos) }) test_that("devResid and aic", { tst.devres <- function(fam, frm) { ff <- glmFamily$new(family=fam) sapply(frm, function(x) { nn <- length(x) wt <- rep.int(1, nn) n <- wt y <- switch(fam$family, binomial = rbinom(nn, 1L, x), gaussian = rnorm(nn, x), poisson = rpois(nn, x), error("Unknown family")) dev <- ff$devResid(y, x, wt) expect_that(fam$dev.resids(y, x, wt), equals(dev)) dd <- sum(dev) expect_that(fam$aic(y, n, x, wt, dd), equals(ff$aic(y, n, x, wt, dd))) }) } tst.devres(binomial(), mubinom) tst.devres(gaussian(), etas) tst.devres(poisson(), etapos) }) test_that("variance", { tst.variance <- function(fam, frm) { ff <- glmFamily$new(family=fam) sapply(frm, function(x) expect_that(fam$variance(x), equals(ff$variance(x)))) } tst.variance(MASS::negative.binomial(1.), etapos) nb1 <- MASS::negative.binomial(1.) cppnb1 <- glmFamily$new(family=nb1) expect_that(cppnb1$theta(), equals(1)) nb2 <- MASS::negative.binomial(2.) cppnb1$setTheta(2) sapply(etapos, function(x) expect_that(cppnb1$variance(x), equals(nb2$variance(x)))) bfam <- glmFamily$new(family=binomial()) if (FALSE) { ## segfaults on MacOS mavericks 3.1.0 ... ?? expect_error(bfam$theta())#, "theta accessor applies only to negative binomial") expect_error(bfam$setTheta(2))#, "setTheta applies only to negative binomial") } }) simfun_gam <- function(ngrp = 50, nrep = 50, shape_gam = 2, intercept = 1, use_simulate = FALSE, seed = NULL) { if (!is.null(seed)) set.seed(seed) dd <- expand.grid(group = 1:ngrp, rep = 1:nrep) if (use_simulate) { dd$y <- simulate(~ 1 + (1 | group), newdata = dd, family = Gamma(link = "log"), newparams = list( theta = 1, beta = 1, sigma = 1/sqrt(shape_gam)))[[1]] return(dd) } b <- rnorm(ngrp) eta <- intercept + b mu <- exp(eta) y <- rgamma(nrow(dd), shape = shape_gam, scale = mu/shape_gam) data.frame(dd, y) } dd1 <- simfun_gam(seed = 101) dd2 <- simfun_gam(seed = 101, use_simulate = TRUE) test_that("simulated Gamma data matches with simulate()", { expect_equal(dd1$y, dd2$y) }) test_that("estimated Gamma shape is correct", { m1 <- glmer(y ~ 1 + (1|group), family = Gamma(link = "log"), data = dd2) shape_val <- 1/sigma(m1)^2 expect_equal(shape_val, 2.0, tolerance = 0.05) expect_equal(shape_val, 1.94511502080571, tolerance = 1e-6) }) simfun_invgauss <- function(ngrp = 50, nrep = 500, lambda = 1, use_simulate = FALSE, seed = NULL) { if (!is.null(seed)) set.seed(seed) dd <- expand.grid(group = 1:ngrp, rep = 1:nrep) if (use_simulate) { dd$y <- simulate(~ 1 + (1 | group), newdata = dd, family = inverse.gaussian(link = "1/mu^2"), newparams = list( theta = c("group.(Intercept)" = 1), beta = c("(Intercept)" = 4), sigma = 1/sqrt(lambda)))[[1]] return(dd) } b <- rnorm(ngrp) eta <- 4 + b mu <- 1/sqrt(eta) y <- statmod::rinvgauss(nrow(dd), mean = mu, shape = lambda) data.frame(dd, y) } ddig1 <- simfun_invgauss(seed = 101, use_simulate = FALSE) ddig2 <- simfun_invgauss(seed = 101, use_simulate = TRUE) test_that("simulated Inverse Gaussian data matches with simulate()", { expect_equal(ddig1$y, ddig2$y) }) test_that("estimated Inverse Gaussian shape is correct", { m1 <- glmer(y ~ 1 + (1 | group), family = inverse.gaussian(link = "1/mu^2"), data = ddig2) shape_val <- 1/sigma(m1)^2 expect_equal(shape_val, 1, tolerance = 0.05) expect_equal(shape_val, 1.032144112298057, tolerance = 1e-6) }) lme4/tests/testthat/test-start.R0000644000176200001440000000701515113136605016356 0ustar liggesusers#context("specifying starting values") ##' Update 'mod', copying .@call and attr(.@frame, "start") from 'from' copysome <- function(mod, from) { stopifnot(all.equal(class(mod), class(from)), isS4(mod)) mod@call <- from@call attr(mod@frame, "start") <- attr(from@frame, "start") mod } ## is "Nelder_Mead" default optimizer? isNM <- formals(lmerControl)$optimizer == "Nelder_Mead" stMsg <- "'start' must be .* a numeric vector .* list" test_that("lmer", { frm <- Reaction ~ Days + (Days|Subject) ctrl <- lmerControl(optCtrl = list(maxfun= if(isNM) 50 else 100)) x <- suppressWarnings(lmer(frm, data=sleepstudy, control=ctrl, REML=FALSE)) x2 <- suppressWarnings(update(x,start=c(1,0,1))) x3 <- suppressWarnings(update(x,start=list(theta=c(1,0,1)))) ff <- update(x,devFunOnly=TRUE) x2@call <- x3@call <- x@call ## hack call component expect_equal(x,x2) expect_equal(x,x3) ## warning on deprecated list ... suppressWarnings(expect_error(update(x, start = "a"), stMsg)) ## misspelled suppressWarnings( expect_error(update(x,start=list(Theta=c(1,0,1))),"incorrect components") ) th0 <- getME(x,"theta") y <- suppressWarnings(update(x,start=th0)) if(isNM) { expect_equal(AIC(x), 1768.025, tolerance=1e-6) expect_equal(AIC(y), 1763.949, tolerance=1e-6) } else { ## only "bobyqa" tested: expect_equal(AIC(x), 1763.939344, tolerance=1e-6) expect_equal(AIC(x), AIC(y)) } if(isNM) expect_equal(suppressWarnings(optimizeLmer(ff,control=list(maxfun=50), start=c(1,0,1))$fval), unname(deviance(x))) expect_equal(suppressWarnings(optimizeLmer(ff,control=list(maxfun=50), start=th0)$fval), unname(deviance(y))) }) test_that("glmer", { ctrl <- glmerControl(optCtrl=list(maxfun=50)) # -> non-convergence warnings x <- suppressWarnings(glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial, control=ctrl)) ## theta only x2 <- suppressWarnings(update(x, start= 1)) x3 <- suppressWarnings(update(x, start= list(theta = 1))) ff <- update(x,devFunOnly=TRUE) x2@call <- x3@call <- x@call ## hack call component expect_equal(x,x2) expect_equal(x,x3) expect_error(update(x, start="a"), stMsg) expect_error(update(x, start=list(Theta=1)), "bad name\\(s\\)") ## test fixef/beta synonymy x3B <- suppressWarnings(update(x, start=list(beta = rep(1,4)))) x3C <- suppressWarnings(update(x, start=list(fixef = rep(1,4)))) ## hack to set calls equal x3B@call <- x3C@call expect_equal(x3B, x3C) ## th0 <- getME(x,"theta") y <- suppressWarnings(update(x, start=th0)) # expect_equal() fails: optinfo -> derivs -> Hessian ## theta and beta x0 <- update(x,nAGQ=0) x4 <- suppressWarnings(update(x, start = list(theta=1, fixef=fixef(x0)))) expect_equal(x, copysome(x4, from=x)) x5 <- suppressWarnings(update(x, start = list(theta=1, fixef=rep(0,4)))) expect_equal(AIC(x5), 221.5823, tolerance=1e-6) x6 <- expect_error(suppressWarnings(update(x, start = list(theta=1, fixef=rep(0,5))), "incorrect number of fixef components")) ## beta only x7 <- suppressWarnings(update(x,start=list(fixef=fixef(x0)))) expect_equal(x, copysome(x7, from=x)) x8 <- suppressWarnings(update(x,start=list(fixef=rep(0,4)))) expect_equal(x5, copysome(x8, from=x5)) }) lme4/tests/testthat/test-lmer.R0000644000176200001440000005416115113136610016160 0ustar liggesusers## (as.function.merMod() assumes it) data("Dyestuff", package = "lme4") data("cbpp", package = "lme4") library(lme4) ## this *is* necessary, for as.function.merMod() ... ## use old (<=3.5.2) sample() algorithm if necessary if ("sample.kind" %in% names(formals(RNGkind))) { suppressWarnings(RNGkind("Mersenne-Twister", "Inversion", "Rounding")) } #context("fitting lmer models") ## is "Nelder_Mead" default optimizer? -- no longer (isNM <- formals(lmerControl)$optimizer == "Nelder_Mead") test_that("lmer", { set.seed(101) d <- data.frame(z=rnorm(200), f=factor(sample(1:10,200, replace=TRUE))) ## Using 'method=*' defunct in 2019-05 (after 6 years of deprecation) ## expect_warning(lmer(z~ 1|f, d, method="abc"),"Use the REML argument") ## expect_warning(lmer(z~ 1|f, d, method="Laplace"),"Use the REML argument") ##sp No '...' anymore ##sp expect_warning(lmer(z~ 1|f, d, sparseX=TRUE),"has no effect at present") expect_error(lmer(z~ 1|f, ddd), "bad 'data': object 'ddd' not found") expect_error(lmer(z~ 1|f), "object 'z' not found") expect_error(lmer(z~ 1|f, d[,1:1000]), "bad 'data': undefined columns selected") expect_is(fm1 <- lmer(Yield ~ 1|Batch, Dyestuff), "lmerMod") expect_is(fm1_noCD <- update(fm1,control=lmerControl(calc.derivs=FALSE)), "lmerMod") expect_equal(VarCorr(fm1),VarCorr(fm1_noCD)) ## backward compatibility version {for optimizer="Nelder-Mead" only}: if(isNM) expect_is(fm1.old <- update(fm1,control=lmerControl(use.last.params=TRUE)), "lmerMod") expect_is(fm1@resp, "lmerResp") expect_is(fm1@pp, "merPredD") expect_that(fe1 <- fixef(fm1), is_equivalent_to(1527.5)) expect_that(VarCorr(fm1)[[1]][1,1], ## "bobyqa" : 1764.050060 equals(1764.0375195, tolerance = 1e-5)) ## back-compatibility ... if(isNM) expect_that(VarCorr(fm1.old)[[1]][1,1], equals(1764.0726543)) expect_that(isREML(fm1), equals(TRUE)) expect_is(REMLfun <- as.function(fm1), "function") expect_that(REMLfun(1), equals(319.792389042002)) expect_that(REMLfun(0), equals(326.023232155879)) expect_that(family(fm1), equals(gaussian())) expect_that(isREML(fm1ML <- refitML(fm1)), equals(FALSE)) expect_that(REMLcrit(fm1), equals(319.654276842342)) expect_that(deviance(fm1ML), equals(327.327059881135)) ## "bobyqa": 49.51009984775 expect_that(sigma(fm1), equals(49.5101272946856, tolerance=1e-6)) if(isNM) expect_that(sigma(fm1.old), equals(49.5100503990048)) expect_that(sigma(fm1ML), equals(49.5100999308089)) expect_that(extractAIC(fm1), equals(c(3, 333.327059881135))) expect_that(extractAIC(fm1ML), equals(c(3, 333.327059881135))) ## "bobyqa": 375.71667627943 expect_that(vcov(fm1) [1,1], equals(375.714676744, tolerance=1e-5)) if(isNM) expect_that(vcov(fm1.old)[1,1], equals(375.72027872986)) expect_that(vcov(fm1ML) [1,1], equals(313.09721874266, tolerance=1e-7)) # was 313.0972246957 expect_is(fm2 <- refit(fm1, Dyestuff2$Yield), "lmerMod") expect_that(fixef(fm2), is_equivalent_to(5.6656)) expect_that(VarCorr(fm2)[[1]][1,1], is_equivalent_to(0)) expect_that(getME(fm2, "theta"), is_equivalent_to(0)) expect_that(X <- getME(fm1, "X"), is_equivalent_to(array(1, c(1, 30)))) expect_is(Zt <- getME(fm1, "Zt"), "dgCMatrix") expect_that(dim(Zt), equals(c(6L, 30L))) expect_that(Zt@x, equals(rep.int(1, 30L))) expect_equal(dimnames(Zt), list(levels(Dyestuff$Batch), rownames(Dyestuff))) ## "bobyqa": 0.8483237982 expect_that(theta <- getME(fm1, "theta"), equals(0.84832031, tolerance=6e-6, check.attributes=FALSE)) if(isNM) expect_that(getME(fm1.old, "theta"), is_equivalent_to(0.848330078)) expect_is(Lambdat <- getME(fm1, "Lambdat"), "dgCMatrix") expect_that(as(Lambdat, "matrix"), is_equivalent_to(diag(theta, 6L, 6L))) expect_is(fm3 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy), "lmerMod") expect_that(getME(fm3,"n_rtrms"), equals(2L)) expect_that(getME(fm3,"n_rfacs"), equals(1L)) expect_equal(getME(fm3, "lower"), c(`Subject.(Intercept)` = 0, Subject.Days = 0)) expect_error(fm4 <- lmer(Reaction ~ Days + (1|Subject), subset(sleepstudy,Subject==levels(Subject)[1])), "must have > 1") expect_warning(fm4 <- lFormula(Reaction ~ Days + (1|Subject), subset(sleepstudy,Subject==levels(Subject)[1]), control=lmerControl(check.nlev.gtr.1="warning")), "must have > 1") expect_warning(fm4 <- lmer(Reaction ~ Days + (1|Subject), subset(sleepstudy,Subject %in% levels(Subject)[1:4]), control=lmerControl(check.nlev.gtreq.5="warning")), "< 5 sampled levels") sstudy9 <- subset(sleepstudy, Days == 1 | Days == 9) expect_error(lmer(Reaction ~ 1 + Days + (1 + Days | Subject), data = sleepstudy, subset = (Days == 1 | Days == 9)), "number of observations \\(=36\\) <= number of random effects \\(=36\\)") expect_error(lFormula(Reaction ~ 1 + Days + (1 + Days | Subject), data = sleepstudy, subset = (Days == 1 | Days == 9)), "number of observations \\(=36\\) <= number of random effects \\(=36\\)") ## with most recent Matrix (1.1-1), should *not* flag this ## for insufficient rank dat <- readRDS(system.file("testdata", "rankMatrix.rds", package="lme4")) expect_is(lFormula(y ~ (1|sample) + (1|day) + (1|day:sample) + (1|operator) + (1|day:operator) + (1|sample:operator) + (1|day:sample:operator), data = dat, control = lmerControl(check.nobs.vs.rankZ = "stop")), "list") ## check scale ss <- within(sleepstudy, Days <- Days*1e6) expect_warning(lmer(Reaction ~ Days + (1|Subject), data=ss), "predictor variables are on very different scales") ## Promote warning to error so that warnings or errors will stop the test: options(warn=2) expect_is(lmer(Yield ~ 1|Batch, Dyestuff, REML=TRUE), "lmerMod") expect_is(lmer(Yield ~ 1|Batch, Dyestuff, start=NULL), "lmerMod") expect_is(lmer(Yield ~ 1|Batch, Dyestuff, verbose=0L), "lmerMod") expect_is(lmer(Yield ~ 1|Batch, Dyestuff, subset=TRUE), "lmerMod") expect_is(lmer(Yield ~ 1|Batch, Dyestuff, weights=rep(1,nrow(Dyestuff))), "lmerMod") expect_is(lmer(Yield ~ 1|Batch, Dyestuff, na.action="na.exclude"), "lmerMod") expect_is(lmer(Yield ~ 1|Batch, Dyestuff, offset=rep(0,nrow(Dyestuff))), "lmerMod") expect_is(lmer(Yield ~ 1|Batch, Dyestuff, contrasts=NULL), "lmerMod") expect_is(lmer(Yield ~ 1|Batch, Dyestuff, devFunOnly=FALSE), "lmerMod") expect_is(lmer(Yield ~ 1|Batch, Dyestuff, control=lmerControl(optimizer="Nelder_Mead")), "lmerMod") expect_is(lmer(Yield ~ 1|Batch, Dyestuff, control=lmerControl()), "lmerMod") ## avoid _R_CHECK_LENGTH_1_LOGIC2_ errors ... if (getRversion() < "3.6.0" || (requireNamespace("optimx", quietly = TRUE) && packageVersion("optimx") > "2018.7.10")) { expect_error(lmer(Yield ~ 1|Batch, Dyestuff, control=lmerControl(optimizer="optimx")),"must specify") expect_is(lmer(Yield ~ 1|Batch, Dyestuff, control=lmerControl(optimizer="optimx", optCtrl=list(method="L-BFGS-B"))), "lmerMod") } expect_error(lmer(Yield ~ 1|Batch, Dyestuff, control=lmerControl(optimizer="junk")), "couldn't find optimizer function") ## disable test ... should be no warning expect_is(lmer(Reaction ~ 1 + Days + (1 + Days | Subject), data = sleepstudy, subset = (Days == 1 | Days == 9), control=lmerControl(check.nobs.vs.rankZ="ignore", check.nobs.vs.nRE="ignore", check.conv.hess="ignore", ## need to ignore relative gradient check too; ## surface is flat so *relative* gradient gets large check.conv.grad="ignore")), "merMod") expect_is(lmer(Reaction ~ 1 + Days + (1|obs), data = transform(sleepstudy,obs=seq(nrow(sleepstudy))), control=lmerControl(check.nobs.vs.nlev="ignore", check.nobs.vs.nRE="ignore", check.nobs.vs.rankZ="ignore")), "merMod") expect_error(lmer(Reaction ~ 1 + Days + (1|obs), data = transform(sleepstudy,obs=seq(nrow(sleepstudy))), "number of levels of each grouping factor")) ## check for errors with illegal input checking options flags <- lme4:::.get.checkingOpts(names(formals(lmerControl))) .t <- lapply(flags, function(OPT) { ## set each to invalid string: ## cat(OPT,"\n") expect_error(lFormula(Reaction~1+Days+(1|Subject), data = sleepstudy, control = do.call(lmerControl, ## Deliberate: fake typo ## vvv setNames(list("warnign"), OPT))), "invalid control level") }) ## disable warning via options options(lmerControl=list(check.nobs.vs.rankZ="ignore",check.nobs.vs.nRE="ignore")) expect_is(fm4 <- lmer(Reaction ~ Days + (1|Subject), subset(sleepstudy,Subject %in% levels(Subject)[1:4])), "merMod") expect_is(lmer(Reaction ~ 1 + Days + (1 + Days | Subject), data = sleepstudy, subset = (Days == 1 | Days == 9), control=lmerControl(check.conv.hess="ignore", check.conv.grad="ignore")), "merMod") options(lmerControl=NULL) ## check for when ignored options are set options(lmerControl=list(junk=1,check.conv.grad="ignore")) expect_warning(lmer(Reaction ~ Days + (1|Subject),sleepstudy), "some options") options(lmerControl=NULL) options(warn=0) expect_error(lmer(Yield ~ 1|Batch, Dyestuff, junkArg=TRUE), "unused argument") expect_warning(lmer(Yield ~ 1|Batch, Dyestuff, control=list()), "passing control as list is deprecated") if(FALSE) ## Hadley broke this expect_warning(lmer(Yield ~ 1|Batch, Dyestuff, control=glmerControl()), "passing control as list is deprecated") ss <- transform(sleepstudy,obs=factor(seq(nrow(sleepstudy)))) expect_warning(lmer(Reaction ~ 1 + (1|obs), data=ss, control=lmerControl(check.nobs.vs.nlev="warning", check.nobs.vs.nRE="ignore")), "number of levels of each grouping factor") ## test deparsing of very long terms inside mkReTrms set.seed(101) longNames <- sapply(letters[1:25], function(x) paste(rep(x,8),collapse="")) tstdat <- data.frame(Y=rnorm(10), F=factor(1:10), matrix(runif(250),ncol=25, dimnames=list(NULL, longNames))) expect_is(lFormula(Y~1+(aaaaaaaa+bbbbbbbb+cccccccc+dddddddd+ eeeeeeee+ffffffff+gggggggg+hhhhhhhh+ iiiiiiii+jjjjjjjj+kkkkkkkk+llllllll|F), data=tstdat, control=lmerControl(check.nobs.vs.nlev="ignore", check.nobs.vs.nRE="ignore", check.nobs.vs.rankZ="ignore")),"list") ## do.call(new,...) bug new <- "foo" expect_is(refit(fm1),"merMod") rm("new") ## test subset-with-( printing from summary fm1 <- lmer(z~1|f,d,subset=(z<1e9)) expect_equal(sum(grepl("Subset: \\(",capture.output(summary(fm1)))),1) ## test messed-up Hessian ## UPDATE: modified this test since we now automatically ignore Hessian ## checks when there is a singular fit fm1 <- lmer(z~ as.numeric(f) + 1|f, d) expect_equal(summary(fm1)$optinfo$conv$lme4$messages, "boundary (singular) fit: see help('isSingular')") expect_null(fm1@optinfo$derivs$Hessian) ## Previous: ##fm1@optinfo$derivs$Hessian[2,2] <- NA ##expect_warning(lme4:::checkConv(fm1@optinfo$derivs, ## coefs=c(1,1), ## ctrl=lmerControl()$checkConv,lbound=0), ## "Problem with Hessian check") ## test ordering of Ztlist names ## this is a silly model, just using it for a case ## where nlevs(RE term 1) < nlevs(RE term 2)x data(cbpp) cbpp <- transform(cbpp,obs=factor(1:nrow(cbpp))) fm0 <- lmer(incidence~1+(1|herd)+(1|obs),cbpp, control=lmerControl(check.nobs.vs.nlev="ignore", check.nobs.vs.rankZ="ignore", check.nobs.vs.nRE="ignore", check.conv.grad="ignore", check.conv.singular="ignore", check.conv.hess="ignore")) fm0B <- update(fm0, .~1+(1|obs)+(1|herd)) expect_equal(names(getME(fm0,"Ztlist")), c("obs.(Intercept)", "herd.(Intercept)")) ## stable regardless of order in formula expect_equal(getME(fm0,"Ztlist"),getME(fm0B,"Ztlist")) ## no optimization (GH #408) fm_noopt <- lmer(z~1|f,d, control=lmerControl(optimizer=NULL)) expect_equal(unname(unlist(getME(fm_noopt,c("theta","beta")))), c(0.244179074357121, -0.0336616441209862)) expect_error(lmer(z~1|f,d, control=lmerControl(optimizer="none")), "deprecated use") my_opt <- function(fn,par,lower,upper,control) { opt <- optim(fn=fn,par=par,lower=lower, upper=upper,control=control,,method="L-BFGS-B") return(list(par=opt$par,fval=opt$value,conv=opt$convergence)) } expect_is(fm_noopt <- lmer(z~1|f,d, control=lmerControl(optimizer=my_opt)),"merMod") ## test verbose option for nloptwrap cc <- capture.output(lmer(Reaction~1+(1|Subject), data=sleepstudy, control=lmerControl(optimizer="nloptwrap", optCtrl=list(xtol_abs=1e-6, ftol_abs=1e-6)), verbose=5)) expect_equal(sum(grepl("^iteration:",cc)),14) }) ## test_that(..) test_that("coef_lmer", { ## test coefficient extraction in the case where RE contain ## terms that are missing from the FE ... set.seed(101) d <- data.frame(resp=runif(100), var1=factor(sample(1:5,size=100,replace=TRUE)), var2=runif(100), var3=factor(sample(1:5,size=100,replace=TRUE))) mix1 <- lmer(resp ~ 0 + var1 + var1:var2 + (1|var3), data=d) c1 <- coef(mix1) expect_is(c1, "coef.mer") cd1 <- c1$var3 expect_is (cd1, "data.frame") n1 <- paste0("var1", 1:5) nn <- c(n1, paste(n1, "var2", sep=":")) expect_identical(names(cd1), c("(Intercept)", nn)) expect_equal(fixef(mix1), setNames(c(0.2703951, 0.3832911, 0.451279, 0.6528842, 0.6109819, 0.4949802, 0.1222705, 0.08702069, -0.2856431, -0.01596725), nn), tolerance= 6e-6)# 64-bit: 6.73e-9 }) test_that("getCall", { ## GH #535 getClass <- function() "foo" expect_is(glmer(round(Reaction) ~ 1 + (1|Subject), sleepstudy, family=poisson), "glmerMod") rm(getClass) }) test_that("better info about optimizer convergence", { set.seed(14) cbpp$var <- rnorm(nrow(cbpp), 10, 10) suppressWarnings(gm2 <- glmer(cbind(incidence, size - incidence) ~ period * var + (1 | herd), data = cbpp, family = binomial, control=glmerControl(optimizer=c("bobyqa","Nelder_Mead"))) ) ## FIXME: with new update, suppressWarnings(update(gm2)) will give ## Error in as.list.environment(X[[i]], ...) : ## promise already under evaluation: recursive default argument reference or earlier problems? op <- options(warn=-1) gm3 <- update(gm2, control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=2))) options(op) cc <-capture.output(print(summary(gm2))) expect_equal(tail(cc,4)[1], "optimizer (Nelder_Mead) convergence code: 0 (OK)") }) #context("convergence warnings etc.") fm1 <- lmer(Reaction~ Days + (Days|Subject), sleepstudy) suppressMessages(fm0 <- lmer(Reaction~ Days + (Days|Subject), sleepstudy[1:20,])) msg_in_output <- function(x, str) { cc <- capture.output(.prt.warn(x)) any(grepl(str , cc)) } test_that("convergence warnings from limited evals", { expect_warning(fm1B <- update(fm1, control=lmerControl(optCtrl=list(maxeval=3))), "convergence code 5") expect_true(msg_in_output(fm1B@optinfo, "convergence code: 5")) expect_warning(fm1C <- update(fm1, control=lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=3))), "maximum number of function evaluations exceeded") expect_true(msg_in_output(fm1C@optinfo, "maximum number of function evaluations exceeded")) ## one extra (spurious) warning here ... expect_warning(fm1D <- update(fm1, control=lmerControl(optimizer="Nelder_Mead",optCtrl=list(maxfun=3))), "failure to converge in 3 evaluations") expect_true(msg_in_output(fm1D@optinfo, "failure to converge in 3 evaluations")) expect_message(fm0D <- update(fm0, control=lmerControl(optimizer="Nelder_Mead",calc.derivs=FALSE)), "boundary") expect_true(msg_in_output(fm0D@optinfo, "(OK)")) }) ## GH 533 test_that("test for zero non-NA cases", { data_bad <- sleepstudy data_bad$Days <- NA_real_ expect_error(lmer(Reaction ~ Days + (1| Subject), data_bad), "0 \\(non-NA\\) cases") }) ## test_that("catch matrix-valued responses in lmer/glmer but not in formulas", { dd <- data.frame(x = rnorm(1000), batch = factor(rep(1:20, each=50))) dd$y <- matrix(rnorm(1e4), ncol = 10) dd$y2 <- matrix(rpois(1e4, lambda = 1), ncol = 10) expect_error(lmer(y ~ x + (1|batch), dd), "matrix-valued") fr <- lFormula(y ~ x + (1|batch), dd)$fr expect_true(is.matrix(model.response(fr))) expect_error(glmer(y ~ x + (1|batch), dd, family = poisson), "matrix-valued") fr <- glFormula(y ~ x + (1|batch), dd, family = poisson)$fr }) test_that("catch matrix-valued responses", { dd <- data.frame(x = rnorm(1000), batch = factor(rep(1:20, each=50))) dd$y <- matrix(rnorm(1e4), ncol = 10) expect_error(lmer(y ~ x + (1|batch), dd), "matrix-valued") }) test_that("update works as expected", { m <- lmer(Reaction ~ Days + (Days || Subject), sleepstudy) expect_equivalent(fitted(update(m, .~.-(0 + Days | Subject))), fitted(lmer(Reaction ~ Days + (1|Subject), sleepstudy))) }) test_that("turn off conv checking for nobs > check.conv.nobsmax", { ## calc derivs and check convergence fm1 <- lmer(Reaction ~ 1 + (1|Days), sleepstudy) nn <- nrow(sleepstudy)-1 ## neither derivs nor conv check fm2 <- update(fm1, control = lmerControl(check.conv.nobsmax = nn)) ## no conv check, do calc derivs fm3 <- update(fm1, control = lmerControl(check.conv.nobsmax = nn, calc.derivs = TRUE)) expect_null(fm2@optinfo$derivs) expect_false(is.null(fm1@optinfo$derivs)) expect_false(is.null(fm3@optinfo$derivs)) expect_equal(fm1@optinfo$conv$lme4, list()) expect_null(fm2@optinfo$conv$lme4) expect_null(fm3@optinfo$conv$lme4) }) test_that("turn off conv checking for npara > check.conv.nparmax", { set.seed(1) n_groups <- 20 n_per_group <- 20 n <- n_groups * n_per_group dat <- data.frame( group = rep(1:n_groups, each = n_per_group), x1 = rnorm(n), x2 = rnorm(n) ) set.seed(101) form <- y ~ 1 + x1 * x2 + (1 + x1|group) dat$y <- simulate(form[-2], ## one-sided formula newdata = dat, family = gaussian, newparams = list(beta = c(-7, 5, -100, 20), theta = c(2.5, 1.4, 6.3), sigma = 2))[[1]] mod1 <- lmer(form, data = dat, control = lmerControl( optimizer = "bobyqa", optCtrl = list(maxfun = 10) )) mod2 <- update(mod1, control = lmerControl( optimizer = "bobyqa", optCtrl = list(maxfun = 10), check.conv.nparmax = 3) ) ## First should give a warning expect_false(is.null(mod1@optinfo$conv$lme4)) ## Second shouldn't be evaluated expect_null(mod2@optinfo$conv$lme4) }) test_that("gradient and Hessian checks are skipped when singular fit occurs",{ set.seed(1) group <- factor(rep(1:3, each = 20)) b <- rnorm(3, mean = 0, sd = 0.01) x <- rnorm(60) y <- x + b[group] + rnorm(60, sd = 1) dat <- data.frame(y, x, group) fm1 <- lmer(y ~ x + (1 | group), data = dat) expect_null(summary(fm1)$optinfo$derivs$gradient) expect_null(summary(fm1)$optinfo$derivs$Hessian) ## Always skipping derivative checks options(lme4.singular.tolerance = 1) fm2 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) expect_null(summary(fm2)$optinfo$derivs$gradient) expect_null(summary(fm2)$optinfo$derivs$Hessian) ## Switching back (in case needed) options(lme4.singular.tolerance = 1e-4) ## force calculating the derivatives even if the fit is singular fm2 <- lmer(y ~ x + (1 | group), control = lmerControl(calc.derivs = TRUE), data = dat) expect_false(is.null(summary(fm2)$optinfo$derivs)) }) lme4/tests/testthat/test-doubleVertNotation.R0000644000176200001440000000070115113136605021043 0ustar liggesusers#context("testing '||' notation for independent ranefs") test_that("basic intercept + slope '||' works", { expect_equivalent( lFormula(Reaction ~ Days + (Days||Subject), sleepstudy)$reTrms, lFormula(Reaction ~ Days + (1|Subject) + (0 + Days|Subject), sleepstudy)$reTrms ) expect_equivalent( fitted(lmer(Reaction ~ Days + (Days||Subject), sleepstudy)), fitted(lmer(Reaction ~ Days + (1|Subject) + (0 + Days|Subject), sleepstudy)) ) }) lme4/tests/testthat/test-summary_testlevel_1.rda0000644000176200001440000133333415103775246021612 0ustar liggesusers‹ě˝x]ĹŐ5|Ý{·%7I×’eIV»*î–{Ç Ťéȶl «InjHč „–ťzO(ˇˇ$ˇˇC˙žsמY÷hŽ$y˙÷űţßĎłYb/­}öÔ33çÜ«UóÖVô^Ű;‹u‰uí.˙í&?Ćş­Ţ{AńäX¬kgůźN±®±^‚ťëËbťşŚy»6Ő4oőř»—ěśWÝRm~#–ü÷)°ł*ëęk*ńsź˛’˛âŠI%S‰„üďbCÄrz¦í¶ŁE.ëŇŁKčjÝ6ÖÖÔm¶ţíÔß쵾®şąyyu}Mč·{4l«ŻiŞ]ŽüľdÍż›¶ˇfcő¶ş–9µ 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U+ŽXŐnůÓ·}tAlřwĚbľůhK°ÜĚď,Šd(˘·…tX,1ŁÉ1Q ^ýż? ±…źýs,?ÁĂQ(žiŹ©(”Č© S’E˘2ŤzG·ŔKc;•©^ë÷ů sJ-áżc±XÇ#ą–ÂLy!ÄŁv˙߸ědRv¦PĺÇł$!ʼn#—‚,)ůҤ ŻŘNŤˇ|J2 \aŹjLČ‘8=ŠŻ 1ćâąü6Ýî;%* š‰h"ŢáUMD‹™&ô·›Ĺ„ÝoVö†•{ż.vwé6źf~cGëĺ–¬ł2GŐ[Zi«‹ĐĹ$}ŮJ¶9—ÉîŕóHî­ŽÝh1מ¸ŃČF‘^ßÓĂa¨č ń±cŃu,y4–_ß ň!®%3ĚÓ¤‡Š¤qBĚç/Ó)ş#·_€ĂĚfT16KfÓŰsOĘ 8â5i^=n;Ĺtuî"”o(š„4†ZWţEř/ĄßrjŹăµ|íI˙©>…*ć›dÇC wâÚČÂJÍlĘđ—”×2ę-®ň€ëm2˙&Ď=yi©“ f“ÚqU´¬…šĐ-w%ďW’+ZŞ%J3Ü%Ę›s&uÁĎ'DűÝ*ţOŃĎďhď}'­~+€0 °Błś=ĄĄŔ ů SnIBÔ[ß5'ç\—?Ó/ĂÖăĚ'ňúŰúukrKáśťßYĂű$EŇŚüšŻÇbV9ÜJ9hW/y ńWŘ0'Ő6É#‰§Ť6ÚXŞ^°E¸˝s(ďžBŻ‹Í„oŚî˝|Îń~8×E”)!2SčBÜ´[ř6Ź˘rŐíĄĽ¸yď ]Ň Ľ¦žH %ÓˇˇkËÍBJŤúŻ"şÄůćW®ZĚ*»wë¦Ő3ĚžQŻ#”@äˇY|JW*‰ărzĺ’¸©$1( ¶Q7™ŐĽÝ‚ż^U_B™$ľHd\,őđ«pY]6úŰaĎş|Ń–µOé%Đ Ü!1˛ňpŁŁéч;źÓ›ä«F’( Äý_Äĺö˘Ä#lme4/tests/testthat/test-factors.R0000644000176200001440000000352715103764661016676 0ustar liggesuserstest_that("factors", { set.seed(101) d <- data.frame(x=runif(1000),y=runif(1000),f1=rep(1:10,each=100),f2=rep(1:10,100)) d2 <- transform(d,f1=factor(f1),f2=factor(f2)) expect_that(lm1 <- lmer(y~x+(1|f1/f2),data=d), is_a("lmerMod")) expect_that(lm2 <- lmer(y~x+(1|f1/f2),data=d2),is_a("lmerMod")) expect_equivalent(lm1,lm2) }) ## this will fail/take a long time unless we handle interactions carefully test_that("savvy interactions", { dd <- data.frame(y = 1:10000, f1 = factor(1:10000), f2 = factor(1:10000)) F1 <- lFormula(y ~ 1 + (1|f1/f2), data =dd, control = lmerControl(check.nobs.vs.nlev = "ignore", check.nobs.vs.nRE = "ignore")) expect_equal(dim(F1$reTrms$Zt), c(20000, 10000)) }) test_that("savvy factor level ordering", { check_f <- function(n = 200, frac = 0.7, fix_order = TRUE, check_order = TRUE) { dd <- expand.grid(f1 = seq(n), f2 = seq(n)) dd <- within(dd, { f1 <- factor(f1, levels = sample(unique(f1))) f2 <- factor(f2, levels = sample(unique(f2))) }) dd <- dd[sample(nrow(dd), size = round(frac*nrow(dd)), replace = FALSE), ] dd <- within(dd, { f12 <- f1:f2 f12d <- droplevels(f12) }) new_levels <- with(dd, levels(`%i%`(f1,f2, fix.order = fix_order))) ## don't want to pay the cost of checking if unneeded {for benchmarking} if (fix_order && check_order) { stopifnot(identical(levels(dd$f12d), new_levels)) } return(TRUE) } ## should fail within check_f() if levels don't match expect_true(check_f(), "'savvy' factor levels match brute-force version") ## library(microbenchmark) ## set.seed(101) ## m1 <- microbenchmark(check_f(fix_order = TRUE, check_order = FALSE), ## check_f(fix_order = FALSE)) }) lme4/tests/testthat/test-rank.R0000644000176200001440000000744215103764661016170 0ustar liggesusers#context("testing fixed-effect design matrices for full rank") test_that("lmerRank", { set.seed(101) n <- 20 x <- y <- rnorm(n) d <- data.frame(x,y, z = rnorm(n), r = sample(1:5, size=n, replace=TRUE), y2 = y + c(0.001, rep(0,n-1))) expect_message(fm <- lmer( z ~ x + y + (1|r), data=d), "fixed-effect model matrix is .*rank deficient") ## test reconstitution of full parameter vector (with NAs) expect_equal(names(fixef(fm,add.dropped=TRUE)), c("(Intercept)","x","y")) expect_equal(fixef(fm,add.dropped=TRUE)[1:2], fixef(fm)) expect_equal(nrow(anova(fm)), 1L) expect_error(lmer( z ~ x + y + (1|r), data=d, control=lmerControl(check.rankX="stop")), "rank deficient") expect_error(lmer( z ~ x + y + (1|r), data=d, control=lmerControl(check.rankX="ignore")), "not positive definite") ## should work: expect_is(lmer( z ~ x + y2 + (1|r), data=d), "lmerMod") d2 <- expand.grid(a=factor(1:4),b=factor(1:4),rep=1:10) n <- nrow(d2) d2 <- transform(d2,r=sample(1:5, size=n, replace=TRUE), z=rnorm(n)) d2 <- subset(d2,!(a=="4" & b=="4")) expect_error(lmer( z ~ a*b + (1|r), data=d2, control=lmerControl(check.rankX="stop")), "rank deficient") expect_message(fm <- lmer( z ~ a*b + (1|r), data=d2), "fixed-effect model matrix is rank deficient") d2 <- transform(d2, ab=droplevels(interaction(a,b))) ## should work: expect_is(fm2 <- lmer( z ~ ab + (1|r), data=d2), "lmerMod") expect_equal(logLik(fm), logLik(fm2)) expect_equal(sum(anova(fm)[, "npar"]), anova(fm2)[, "npar"]) expect_equal(sum(anova(fm)[, "Sum Sq"]), anova(fm2)[, "Sum Sq"]) }) test_that("glmerRank", { set.seed(111) n <- 100 x <- y <- rnorm(n) d <- data.frame(x, y, z = rbinom(n,size=1,prob=0.5), r = sample(1:5, size=n, replace=TRUE), y2 = ## y + c(0.001,rep(0,n-1)), ## too small: get convergence failures ## FIXME: figure out how small a difference will still fail? rnorm(n)) expect_message(fm <- glmer( z ~ x + y + (1|r), data=d, family=binomial), "fixed-effect model matrix is rank deficient") expect_error(glmer( z ~ x + y + (1|r), data=d, family=binomial, control=glmerControl(check.rankX="stop")), "rank deficient.*rank.X.") expect_is(glmer( z ~ x + y2 + (1|r), data=d, family=binomial), "glmerMod") }) test_that("nlmerRank", { set.seed(101) n <- 1000 nblock <- 15 x <- abs(rnorm(n)) y <- rnorm(n) z <- rnorm(n,mean=x^y) r <- sample(1:nblock, size=n, replace=TRUE) d <- data.frame(x,y,z,r) ## save("d","nlmerRank.RData") ## see what's going on with difference in contexts fModel <- function(a,b) (exp(a)*x)^(b*y) fModf <- deriv(body(fModel), namevec = c("a","b"), func = fModel) fModel2 <- function(a,b,c) (exp(a+c)*x)^(b*y) fModf2 <- deriv(body(fModel2), namevec = c("a","b","c"), func = fModel2) ## should be OK: fails in test mode? nlmer(y ~ fModf(a,b) ~ a|r, d, start = c(a=1,b=1)) ## FIXME: this doesn't get caught where I expected expect_error(nlmer(y ~ fModf2(a,b,c) ~ a|r, d, start = c(a=1,b=1,c=1)),"Downdated VtV") }) test_that("ranksim", { set.seed(101) x <- data.frame(id = factor(sample(10, 100, replace = TRUE))) x$y <- rnorm(nrow(x)) x$x1 <- 1 x$x2 <- ifelse(x$y<0, rnorm(nrow(x), mean=1), rnorm(nrow(x), mean=-1)) m <- suppressMessages(lmer(y ~ x1 + x2 + (1 | id), data=x)) expect_equal(simulate(m, nsim = 1, use.u = FALSE, newdata=x, seed=101), simulate(m, nsim = 1, use.u = FALSE, seed=101)) }) lme4/tests/testthat/test-NAhandling.R0000644000176200001440000002016215103764661017232 0ustar liggesusersstopifnot(require("testthat"), require("lme4")) #context("NA (and Inf) handling") ## Modified sleepstudy data : sleepst.a <- sleepstudy rownames(sleepst.a) <- paste0("a", rownames(sleepstudy)) sleepstudyNA <- within(sleepst.a, Reaction[1:3] <- NA) sleepstudyNA2 <- within(sleepst.a, Days[1:3] <- NA) sleepInf <- within(sleepstudy, Reaction[Reaction > 400] <- Inf) ## Modified cake data : cakeNA <- rbind(cake, tail(cake,1)) cakeNA[nrow(cakeNA), "angle"] <- NA ## Create new data frame with some NAs in fixed effect cakeNA.X <- within(cake, temp[1:5] <- NA) ## NA values in random effects -- should get treated cakeNA.Z <- within(cake, replicate[1:5] <- NA) test_that("naming", { ## baseline model fm1 <- lmer(Reaction~Days+(Days|Subject), sleepst.a) ## default: na.omit fm2 <- update(fm1, data=sleepstudyNA, control=lmerControl(check.conv.grad="ignore")) expect_equal(head(names(fitted(fm1))), paste0("a",1:6)) expect_equal(head(names(fitted(fm2))), paste0("a",4:9)) expect_equal(names(predict(fm2)), names(fitted(fm2))) expect_equal(length(p1 <- predict(fm2)), 177) ## predict with na.exclude -> has 3 NA's, but otherwise identical: expect_equal(length(p2 <- predict(fm2, na.action=na.exclude)), 180) expect_identical(p1, p2[!is.na(p2)]) expect_equal(length((s1 <- simulate(fm1,1))[[1]]),180) expect_equal(length((s2 <- simulate(fm2,1))[[1]]),177) expect_equal(head(rownames(s1)),paste0("a",1:6)) expect_equal(head(rownames(s2)),paste0("a",4:9)) ## test simulation expect_is(attr(simulate(fm2),"na.action"),"omit") expect_is(refit(fm2,simulate(fm2)),"merMod") expect_equal(fixef(fm2), fixef(refit(fm2, sleepstudyNA$Reaction)), tolerance = 1e-5) fm2ex <- update(fm2, na.action=na.exclude) expect_equal(nrow(ss2 <- simulate(fm2ex)),180) expect_is(refit(fm2,ss2[[1]]),"merMod") ## issue #197, 18 new subjects; some with NA in y d2 <- sleepstudyNA[c(1:180, 1:180),] d2[,"Subject"] <- factor(rep(1:36, each=10)) d2[d2$Subject == 19, "Reaction"] <- NA expect_equal(dim( simulate(fm1, newdata=d2, allow.new.levels=TRUE) ), c(360,1)) ## na.pass (pretty messed up) expect_error(update(fm1,data=sleepstudyNA, control=lmerControl(check.conv.grad="ignore"), na.action=na.pass), "NA/NaN/Inf in 'y'") sleepstudyNA2 <- within(sleepst.a, Days[1:3] <- NA) expect_error(fm4 <- update(fm1, data = sleepstudyNA2, control=lmerControl(check.conv.grad="ignore"), na.action=na.pass),"NA in Z") expect_is(suppressWarnings(confint(fm2,method="boot",nsim=3, quiet=TRUE)),"matrix") expect_error(update(fm1, data = sleepstudyNA2, control = lmerControl(check.conv.grad="ignore"), na.action = na.pass), "NA in Z") expect_is(suppressWarnings( ci2 <- confint(fm2, method="boot", nsim=3, quiet=TRUE)), "matrix") }) test_that("other_NA", { expect_error(lmer(Reaction ~ Days + (Days | Subject), sleepInf), "\\") fm0 <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake) ## NA's in response : fm1 <- update(fm0, data = cakeNA) expect_true(all.equal( fixef(fm0), fixef(fm1))) expect_true(all.equal(VarCorr(fm0),VarCorr(fm1))) expect_true(all.equal( ranef(fm0), ranef(fm1))) fm1_omit <- update(fm1, na.action = na.omit) fm1_excl <- update(fm1, na.action = na.exclude) expect_error(update(fm1, na.action = na.pass), "NA/NaN") expect_error(update(fm1, na.action = na.fail), "missing values in object") fm1_omit@call <- fm1@call ## <- just for comparing: expect_equal(fm1, fm1_omit) expect_equal(length(fitted(fm1_omit)), 270) expect_equal(length(fitted(fm1_excl)), 271) expect_true(is.na(tail(predict(fm1_excl),1))) ## test predict.lm d <- data.frame(x = 1:10, y = c(rnorm(9),NA)) lm1 <- lm(y~x, data=d, na.action=na.exclude) expect_is(predict(lm1), "numeric") expect_equal(1, sum(is.na(predict(lm1, newdata = data.frame(x=c(1:4,NA)))))) ## Triq examples ... m.lmer <- lmer (angle ~ temp + (1 | recipe) + (1 | replicate), data=cake) ## NAs in fixed effect p1_pass <- predict(m.lmer, newdata=cakeNA.X, re.form=NA, na.action=na.pass) expect_true(length(p1_pass)==nrow(cakeNA.X)) expect_true(all(is.na(p1_pass[1:5]))) p1_omit <- predict(m.lmer, newdata=cakeNA.X, re.form=NA, na.action=na.omit) p1_exclude <- predict(m.lmer, newdata=cakeNA.X, re.form=NA, na.action=na.exclude) expect_true(length(p1_omit)==nrow(na.omit(cakeNA.X))) expect_true(length(p1_exclude)==nrow(cakeNA.X)) expect_true(all.equal(c(na.omit(p1_exclude)),p1_omit)) expect_error(predict(m.lmer, newdata=cakeNA.X, re.form=NA, na.action=na.fail), "missing values in object") ## now try it with re.form==NULL p2_pass <- predict(m.lmer, newdata=cakeNA.X, re.form=NULL, na.action=na.pass) expect_true(length(p2_pass)==nrow(cakeNA.X)) expect_true(all(is.na(p2_pass[1:5]))) p2_omit <- predict(m.lmer, newdata=cakeNA.X, re.form=NULL, na.action=na.omit) p2_exclude <- predict(m.lmer, newdata=cakeNA.X, re.form=NULL, na.action=na.exclude) expect_true(length(p2_omit)==nrow(na.omit(cakeNA.X))) expect_true(all.equal(c(na.omit(p2_exclude)),p2_omit)) expect_error(predict(m.lmer, newdata=cakeNA.X, re.form=NULL, na.action=na.fail), "missing values in object") ## experiment with NA values in random effects -- should get treated expect_error(predict(m.lmer, newdata=cakeNA.Z, re.form=NULL), "NAs are not allowed in prediction data") p4 <- predict(m.lmer, newdata=cakeNA.Z, re.form=NULL, allow.new.levels=TRUE) p4B <- predict(m.lmer, newdata=cakeNA.Z, re.form=~1|recipe, allow.new.levels=TRUE) expect_true(all.equal(p4[1:5],p4B[1:5])) p4C <- predict(m.lmer, newdata=cakeNA.Z, re.form=NA) d <- data.frame(x=runif(100),f=factor(rep(1:10,10))) set.seed(101) u <- rnorm(10) d <- transform(d,y=rnorm(100,1+2*x+u[f],0.2)) d0 <- d d[c(3,5,7),"x"] <- NA ## 'omit' and 'exclude' are the only choices under which ## we will see NA values in the results fm0 <- lmer(y~x+(1|f), data=d0) ## no 'na.action' attribute because no NAs in this data set expect_equal(attr(model.frame(fm0),"na.action"),NULL) fm1 <- update(fm0, data=d) ## no NAs in predict or residuals because na.omit expect_false(any(is.na(predict(fm1)))) expect_false(any(is.na(residuals(fm1)))) fm2 <- update(fm1,na.action="na.exclude") ## no NAs in predict or residuals because na.omit nNA <- sum(is.na(d$x)) expect_equal(sum(is.na(predict(fm2))),nNA) expect_equal(sum(is.na(residuals(fm2))),nNA) expect_error(fm3 <- lmer(y~x+(1|f), data=d, na.action="na.pass"), "(Error in qr.default|NA/NaN/Inf in foreign function call)") expect_is(refit(fm0),"merMod") expect_is(refit(fm1),"merMod") expect_is(refit(fm2),"merMod") ## GH 420: NAs in training data should *not* get ## carried over into predictions! fm4 <- lmer(Reaction~Days+(1|Subject),sleepstudyNA2) pp4 <- predict(fm4,newdata=sleepstudy) expect_equal(length(pp4),nrow(sleepstudy)) expect_equal(sum(is.na(pp4)),0) }) test_that("NAs in fitting data ignored in newdata with random.only=TRUE", { set.seed(101) dd <- data.frame(x=c(rnorm(199),NA),y=rnorm(200), f=factor(rep(1:10,each=20)), g=factor(rep(1:20,each=10))) m1 <- lmer(y~x+(1|f)+(1|g),data=dd,na.action=na.exclude) expect_equal(length(predict(m1,newdata=dd[1:5,],random.only=TRUE)),5) nd.NA <- dd[1:5,] nd.NA$x[5] <- NA ## ?? not *quite* sure what should happen here ... predict(m1,newdata=nd.NA,random.only=TRUE) }) lme4/tests/testthat/test-predict.R0000644000176200001440000006536115103764665016677 0ustar liggesuserslibrary("lattice") testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 ## use old (<=3.5.2) sample() algorithm if necessary if ("sample.kind" %in% names(formals(RNGkind))) { suppressWarnings(RNGkind("Mersenne-Twister", "Inversion", "Rounding")) } do.plots <- TRUE L <- load(system.file("testdata/lme-tst-fits.rda", package="lme4", mustWork=TRUE)) if (getRversion() > "3.0.0") { ## saved fits are not safe with old R versions gm1 <- fit_cbpp_1 fm1 <- fit_sleepstudy_1 fm2 <- fit_sleepstudy_2 fm3 <- fit_penicillin_1 fm4 <- fit_cake_1 } else { gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) fm1 <- lmer(Reaction ~ Days + (1|Subject), sleepstudy) fm2 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) fm3 <- lmer(diameter ~ (1|plate) + (1|sample), Penicillin) fm4 <- lmer(angle ~ temp + recipe + (1 | replicate), data=cake) } if (testLevel>1) { #context("predict") test_that("fitted values", { p0 <- predict(gm1) p0B <- predict(gm1, newdata=cbpp) expect_equal(p0, p0B, tolerance=2e-5) ## ? not sure why high tolerance necessary ? works OK on Linux/R 3.5.0 ## fitted values, unconditional (level-0) p1 <- predict(gm1, re.form=NA) expect_true(length(unique(p1))==length(unique(cbpp$period))) ## fitted values, random-only p1R <- predict(gm1, random.only=TRUE) expect_equal(p1+p1R,p0) if (do.plots) matplot(cbind(p0,p1),col=1:2,type="b") ## neither fixed nor random -- all zero expect_equal(unique(predict(gm1,re.form=NA,random.only=TRUE)),0) }) test_that("predict with newdata", { newdata <- with(cbpp, expand.grid(period=unique(period),herd=unique(herd))) ## new data, all RE p2 <- predict(gm1,newdata) ## new data, level-0 p3 <- predict(gm1,newdata, re.form=NA) p3R <- predict(gm1,newdata, random.only=TRUE) expect_equal(p3+p3R,p2) if (do.plots) matplot(cbind(p2,p3),col=1:2,type="b") }) test_that("predict on response scale", { p0 <- predict(gm1) p5 <- predict(gm1,type="response") expect_equal(p5, plogis(p0)) }) test_that("predict with newdata and RE", { newdata <- with(cbpp,expand.grid(period=unique(period),herd=unique(herd))) ## explicitly specify RE p2 <- predict(gm1,newdata) p4 <- predict(gm1,newdata, re.form=~(1|herd)) expect_equal(p2, p4) }) test_that("effects of new RE levels", { newdata <- with(cbpp, expand.grid(period=unique(period), herd=unique(herd))) newdata2 <- rbind(newdata, data.frame(period=as.character(1:4), herd=rep("new",4))) expect_error(predict(gm1, newdata2), "new levels detected in newdata: new") newdata3 <- rbind(newdata, data.frame(period=as.character(1), herd = paste0("new", 1:50))) expect_error(predict(gm1, newdata3), "new levels detected in newdata: new1, new2,") p2 <- predict(gm1, newdata) p6 <- predict(gm1, newdata2, allow.new.levels=TRUE) expect_equal(p2, p6[1:length(p2)]) ## original values should match ## last 4 values should match unconditional values expect_true(all(tail(p6,4) == predict(gm1, newdata=data.frame(period=factor(1:4)), re.form=NA))) }) test_that("multi-group model", { ## fitted values p0 <- predict(fm3) expect_equal(head(round(p0,4)), c(`1` = 25.9638, `2` = 22.7663, `3` = 25.7147, `4` = 23.6799, `5` = 23.7629, `6` = 20.773)) ## fitted values, unconditional (level-0) p1 <- predict(fm3, re.form=NA) expect_equal(unique(p1),22.9722222222251) if (do.plots) matplot(cbind(p0,p1),col=1:2,type="b") }) test_that("multi-group model with new data", { newdata <- with(Penicillin,expand.grid(plate=unique(plate),sample=unique(sample))) ## new data, all RE p2 <- predict(fm3, newdata) ## new data, level-0 p3 <- predict(fm3, newdata, re.form=NA) ## explicitly specify RE p4 <- predict(fm3, newdata, re.form= ~(1|plate)+(~1|sample)) p4B <- predict(fm3, newdata, re.form= ~(1|sample)+(~1|plate)) ## **** expect_equal(p2,p4) expect_equal(p4,p4B) p5 <- predict(fm3,newdata, re.form=~(1|sample)) p6 <- predict(fm3,newdata, re.form=~(1|plate)) if (do.plots) matplot(cbind(p2,p3,p5,p6),type="b",lty=1,pch=16) }) test_that("random-slopes model", { p0 <- predict(fm2) p1 <- predict(fm2, re.form=NA) ## linear model, so results should be identical patterns but smaller -- ## not including intermediate days newdata <- with(sleepstudy,expand.grid(Days=range(Days),Subject=unique(Subject))) newdata$p2 <- predict(fm2,newdata) newdata$p3 <- predict(fm2,newdata, re.form=NA) newdata$p4 <- predict(fm2,newdata, re.form=~(0+Days|Subject)) newdata$p5 <- predict(fm2,newdata, re.form=~(1|Subject)) ## reference values from an apparently-working run refval <- structure( list(Days = c(0, 9, 0, 9, 0, 9), Subject = structure(c(1L, 1L, 2L, 2L, 3L, 3L), .Label = c("308", "309", "310", "330", "331", "332", "333", "334", "335", "337", "349", "350", "351", "352", "369", "370", "371", "372"), class = "factor"), p2 = c(253.663652396798, 430.66001930835, 211.006415533628, 227.634788908917, 212.444742696829, 257.61053840953), p3 = c(251.405104848485, 345.610678484848, 251.405104848485, 345.610678484848, 251.405104848485, 345.610678484848), p4 = c(251.405104848485, 428.401471760037, 251.405104848485, 268.033478223774, 251.405104848485, 296.570900561186), p5 = c(253.663652396798, 347.869226033161, 211.006415533628, 305.211989169991, 212.444742696829, 306.650316333193)), out.attrs = list(dim = c(Days = 2L, Subject = 18L), dimnames = list( Days = c("Days=0", "Days=9"), Subject = c("Subject=308", "Subject=309", "Subject=310", "Subject=330", "Subject=331", "Subject=332", "Subject=333", "Subject=334", "Subject=335", "Subject=337", "Subject=349", "Subject=350", "Subject=351", "Subject=352", "Subject=369", "Subject=370", "Subject=371", "Subject=372")) ), row.names = c(NA, 6L), class = "data.frame") expect_equal(head(newdata), refval, tol=5e-7) }) test_that("predict and plot random slopes", { tmpf <- function(data,...) { data$Reaction <- predict(fm2,data,...) if (do.plots) xyplot(Reaction~Days,group=Subject,data=data,type="l") return(unname(head(round(data$Reaction,3)))) } expect_equal(tmpf(sleepstudy),c(253.664, 273.33, 292.996, 312.662, 332.329, 351.995)) expect_equal(tmpf(sleepstudy, re.form=NA), c(251.405, 261.872, 272.34, 282.807, 293.274, 303.742)) expect_equal(tmpf(sleepstudy, re.form= ~(0+Days|Subject)), c(251.405, 271.071, 290.738, 310.404, 330.07, 349.736)) expect_equal(tmpf(sleepstudy, re.form= ~(1|Subject)), c(253.664, 264.131, 274.598, 285.066, 295.533, 306)) }) test_that("fewer random effect levels than original", { ## from 'Colonel Triq' summary(fm4) ## replicate 1 only appears in rows 1:18. ## rownames(cake[cake$replicate==1,]) predict(fm4, newdata=cake[-1:-17,], re.form=~ (1 | replicate)) predict(fm4, newdata=cake[-1:-18,], re.form=NA) predict(fm4, newdata=cake[-1:-18,], re.form=~ (1 | replicate)) predict(fm4, newdata=cake[-1:-18,], re.form=~ (1 | replicate), allow.new.levels=TRUE) ## p0 <- predict(fm1,newdata=data.frame(Days=6,Subject=c("308","309"))) p1 <- predict(fm1,newdata=data.frame(Days=rep(6,4), Subject=c("308","309"))) expect_equal(rep(unname(p0),2),unname(p1)) p2 <- predict(fm1,newdata=data.frame(Days=6,Subject="308")) nd <- data.frame(Days=6, Subject=factor("308",levels=levels(sleepstudy$Subject))) p3 <- predict(fm1,newdata=nd) expect_equal(p2,p3) expect_equal(p2,p0[1]) }) test_that("only drop columns when using new data", { ## Stack Overflow 34221564: ## should only drop columns from model matrix when using *new* data ## NB: Fit depends on optimizer somewhat: "nloptwrap" is really better than "bobyqa" library(splines) sleep <- sleepstudy #get the sleep data set.seed(1234567) sleep$age <- as.factor(sample(1:3,length(sleep),rep=TRUE)) form1 <- Reaction ~ Days + ns(Days, df=4) + age + Days:age + (Days | Subject) m4 <- lmer(form1, sleep) # fixed-effect model matrix is rank deficient so dropping 1 column / coefficient expect_lte(REMLcrit(m4), 1713.171) # FIXME !? why this regression?? had 1700.6431; "bobyqa" gave 1713.171 expect_equal(unname(head(predict(m4, re.form=NA))), c(255.203, 259.688, 265.71, 282.583, 294.784, 304.933), tolerance = 0.008) }) test_that("only look for columns that exist in re.form", { ## GH 457 set.seed(101) n <- 200 dd <- data.frame(x=1:n, f=factor(rep(1:10,n/10)), g=factor(rep(1:20,each=n/20)), h=factor(rep(1:5,n/5)), y=rnorm(n)) m1 <- lmer(y~1 + f + (1|h/f) + (poly(x,2)|g), data=dd, control=lmerControl(calc.derivs=FALSE)) expect_equal(unname(predict(m1,re.form= ~1 | h/f, newdata=dd[1,])), 0.14786, tolerance=1e-4) expect_equal(unname(predict(m1,re.form= ~poly(x,2) | g, newdata=dd[1,])), 0.1533, tolerance=.001) ## *last* RE not included (off-by-one error) m1B <- lmer(y~1 + f + (1|g) + (1|h), data=dd, control=lmerControl(calc.derivs=FALSE)) expect_equal(unname(predict(m1B,re.form=~(1|g),newdata=data.frame(f="1",g="2"))),0.1512895,tolerance=1e-5) set.seed(101) n <- 100 xx <- c("r1", "r2", "r3", "r4", "r5") xxx <- c("e1", "e2", "e3") p <- 0.3 School <- factor(sample(xxx, n, replace=TRUE), levels=xxx, ordered=FALSE) Rank <- factor(sample(xx, n, replace=TRUE), levels=xx, ordered=FALSE) df1 <- data.frame( ID = as.integer(runif(n, min = 1, max = n/7)), xx1 = runif(n, min = 0, max = 10), xx2 = runif(n, min = 0, max = 10), xx3 = runif(n, min = 0, max = 10), School, Rank, yx = as.factor(rbinom(n, size = 1, prob = p)) ) df1 <- df1[order(df1$ID, decreasing=FALSE),] mm2 <- glmer(yx ~ xx1 + xx2 + xx3 + Rank + (1 | ID) + (1 | School / Rank), data = df1, family = "binomial",control = glmerControl(calc.derivs =FALSE)) n11 <- data.frame(School= factor("e1", levels = levels(df1$School),ordered=FALSE), Rank = factor("r1", levels = levels(df1$Rank), ordered=FALSE), xx1=8.58, xx2=8.75, xx3=7.92) expect_equal(unname(predict(mm2, n11, type="response",re.form= ~(1 | School / Rank))), 0.1174628,tolerance=1e-5) ## bad factor levels mm3 <- update(mm2, . ~ . - (1|ID)) n12 = data.frame(School="e3",Rank="r2",xx1=8.58,xx2=8.75,xx3=7.92) expect_equal(unname(predict(mm3, n12, type="response")),0.1832894,tolerance=1e-5) ## GH #452 ## FIXME: would like to find a smaller/faster example that would test the same warning (10+ seconds) set.seed(101) n <- 300 df2 <- data.frame( xx1 = runif(n, min = 0, max = 10), xx2 = runif(n, min = 0, max = 10), xx3 = runif(n, min = 0, max = 10), School = factor(sample(xxx, n,replace=TRUE)), Rank = factor(sample(xx, n, replace=TRUE)), yx = as.factor(rbinom(n, size = 1, prob = p)) ) mm4 <- suppressWarnings(glmer(yx ~ xx1 + xx2 + xx3 + Rank + (Rank|School), data = df2, family = "binomial",control = glmerControl(calc.derivs =FALSE))) ## set tolerance to 0.1 (!) to pass win-builder on R-devel/i386 (only: ## tolerance = 3e-5 is OK for other combinations of (R-release, R-devel) x (i386,x64) expect_equal(unname(predict(mm4, n11, type="response")), 0.2675081, tolerance=0.1) }) test_that("simulation works with non-factor", { set.seed(12345) dd <- data.frame(a=gl(10,100), b = rnorm(1000)) test2 <- suppressMessages(simulate(~1+(b|a), newdata=dd, family=poisson, newparams= list(beta = c("(Intercept)" = 1), theta = c(1,1,1)))) expect_is(test2,"data.frame") }) set.seed(666) n <- 500 df <- data.frame(y=statmod::rinvgauss(n, mean=1, shape=2), id=factor(1:20)) model_fit <- glmer(y ~ 1 + (1|id), family = inverse.gaussian(link = "inverse"), data = df, control=glmerControl(check.conv.singular="ignore")) test_that("simulation works for inverse gaussian", { expect_equal(mean(simulate(model_fit)[[1]]), 1.02704392575914, tolerance=1e-5) }) test_that("simulation complains appropriately about bad family", { ig <- inverse.gaussian() ig$family <- "junk" model_fit2 <- glmer(y ~ 1 + (1|id), family = ig, data = df, control=glmerControl(check.conv.singular="ignore")) expect_error(simulate(model_fit2),"simulation not implemented for family") }) test_that("prediction from large factors", { set.seed(101) N <- 50000 X <- data.frame(y=rpois(N, 5), obs=as.factor(1:N)) fm <- glmer(y ~ (1|obs), family="poisson", data=X, control=glmerControl(check.conv.singular="ignore")) ## FIXME: weak tests. The main issue here is that these should ## be reasonably speedy and non-memory-hogging, but those are ## hard to test portably ... expect_is(predict(fm, re.form=~(1|obs)), "numeric") expect_is(predict(fm, newdata=X), "numeric") }) test_that("prediction with gamm4", { if (suppressWarnings(requireNamespace("gamm4"))) { ## loading gamm4 warngs "replacing previous import 'Matrix::update' by 'lme4::update' when loading 'gamm4'" ## from ?gamm4 set.seed(0) ## simulate 4 term additive truth dat <- mgcv::gamSim(1,n=400,scale=2,verbose=FALSE) ## Now add 20 level random effect `fac'... dat$fac <- fac <- as.factor(sample(1:20,400,replace=TRUE)) dat$y <- dat$y + model.matrix(~fac-1)%*%rnorm(20)*.5 br <- gamm4::gamm4(y~s(x0)+x1+s(x2),data=dat,random=~(1|fac)) expect_warning(ss <- simulate(br$mer), "modified RE names") expect_equal(dim(ss), c(400,1)) } }) test_that("prediction with spaces in variable names", { cbpp$`silly period` <- cbpp$period m <- glmer(cbind(incidence,size-incidence) ~ `silly period` + (1|herd), family=binomial, data=cbpp) expect_equal(round(head(predict(m)),3), c(`1` = -0.809, `2` = -1.801, `3` = -1.937, `4` = -2.388, `5` = -1.697, `6` = -2.689)) }) if (requireNamespace("statmod")) { test_that("simulate with rinvgauss", { dd <- data.frame(f=factor(rep(1:20,each=10))) dd$y <- simulate(~1+(1|f), seed=101, family=inverse.gaussian, newdata=dd, ## ?? gives NaN (sqrt(eta)) for low beta ? newparams=list(beta=5,theta=1,sigma=1))[[1]] suppressMessages(m <- glmer(y~1+(1|f), family=inverse.gaussian, data=dd)) set.seed(101) expect_equal(head(unlist(simulate(m))), c(sim_11 = 0.451329390087728, sim_12 = 0.629516371309772, sim_13 = 0.481236633500098, sim_14 = 0.170060386109077, sim_15 = 0.258742371516342, sim_16 = 0.949617440586848)) }) } ## GH 631 test_that("sparse contrasts don't mess up predict()", { dd <- expand.grid(f = factor(1:101), rep1 = factor(1:2), rep2 = 1:2) dd$y <- suppressMessages(simulate(~1 + (rep1|f), seed = 101, newdata = dd, newparams = list(beta = 1, theta = rep(1,3), sigma = 1), family = gaussian)[[1]]) m1 <- lmer( y ~ 1 + (1|f), data = dd) p1 <- predict(m1) p2 <- predict(m1, newdata = dd) expect_identical(p1, p2) }) } ## testLevel > 1 test_that("prediction with . in formula + newdata", { set.seed(101) mydata <- data.frame( groups = rep(1:3, each = 100), x = rnorm(300), dv = rnorm(300) ) train_subset <- sample(1:300, 300 * .8) train <- mydata[train_subset,] test <- mydata[-train_subset,] mod <- lmer(dv ~ . - groups + (1 | groups), data = train) p1 <- predict(mod, newdata = test) mod2 <- lmer(dv ~ x + (1 | groups), data = train) p2 <- predict(mod2, newdata = test) expect_identical(p1, p2) }) test_that("simulate with a factor with one level", { set.seed(1241) y <- factor(c(rep(0,1000), 1, rep(0,1000), 1), levels = c("0","1")) x <- rep(c("A","B"),each = 1001) mod <- glmer(y ~ 1 + (1|x),family = binomial, control = glmerControl(check.conv.singular = "ignore")) s <- simulate(mod,newdata = data.frame(x = "A"), nsim = 10) ## very low mean, all simulated values zero expect_true(all(s == 0)) }) test_that("prediction standard error", { # note that predict.lm returns a list with # fit, se.fit, df, residual.scale mod1 <- lmer(Petal.Width ~ Sepal.Length + (1 | Species), iris) p1 <- predict(mod1, se.fit = TRUE) p2 <- predict(mod1, se.fit = TRUE, newdata = iris) p3 <- predict(mod1, se.fit = TRUE, re.form = NA, newdata = iris) p4 <- predict(mod1, se.fit = TRUE, re.form = NA) p5 <- predict(mod1, se.fit = TRUE, re.form = ~(1 | Species)) p6 <- predict(mod1, se.fit = TRUE, re.form = ~(1 | Species), newdata = iris) p7 <- predict(mod1, se.fit = TRUE, newdata = iris, random.only = TRUE) p8 <- predict(mod1, se.fit = TRUE, re.form = ~(1 | Species), random.only = TRUE) p9 <- predict(mod1, se.fit = TRUE, re.form = ~(1 | Species), newdata = iris, random.only = TRUE) expect_equal(unname(head(p1$se.fit)), c(0.0271816400250223, 0.0272298862268211, 0.0286188379907626, 0.0297645467444413, 0.0270330515271627, 0.0295876265523127)) # re.form = NA expect_equal(unname(head(p3$se.fit)), c(0.451147865048879, 0.451497971849052, 0.451930732595154, 0.452178035807842, 0.451312573619068, 0.450778089845166)) # random.only may need checking -- tolerance maybe too high for this? expect_equal(unname(p8$se.fit), rep(0.451569712647126, 150), tolerance = 0.001) expect_equal(p1, p2) expect_equal(p3, p4) expect_equal(p1, p5) expect_equal(p1, p6) expect_equal(p7, p8) expect_equal(p7, p9) }) test_that("NA + re.form = NULL + simulate OK (GH #737)", { d <- lme4::sleepstudy d$Reaction[1] <- NA fm1 <- lmer(Reaction ~ Days + (Days | Subject), d) ss <- simulate(fm1, seed = 101, re.form = NULL)[[1]] expect_equal(c(head(ss)), c(266.139101412856, 308.148180398426, 296.081377893883, 338.367909016478, 360.294339946214, 401.91050930589)) ss0 <- simulate(fm1, seed = 101, re.form = NA)[[1]] expect_equal(length(ss), length(ss0)) ## correct dimensions with na.exclude as well ? fm2 <- update(fm1, na.action = na.exclude) ss2 <- simulate(fm2, seed = 101, re.form = NULL)[[1]] ss3 <- simulate(fm2, seed = 101, re.form = NA)[[1]] expect_equal(length(ss2), nrow(d)) expect_equal(length(ss3), nrow(d)) }) ## GH 691 parts 1 and 2 test_that("predict works with factors in left-out REs", { set.seed(101) df2 <- data.frame(yield = rnorm(100), lc = factor(rep(1:2, 50)), g1 = factor(rep(1:10, 10)), g3 = factor(rep(1:10, each = 10))) m1B <- suppressWarnings(lmer(yield ~ 1 + ( 1 | g1) + (lc |g3), data = df2, control = lmerControl(check.conv.singular = "ignore"))) expect_equal(head(predict(m1B, re.form = ~(1|g1)),1), c(`1` = 0.146787496519465), tolerance = 1e-4) }) test_that("predict works with dummy() in left-out REs", { set.seed(101) df3 <- data.frame(v1 = rnorm(100), v3 = factor(rep(1:10, each = 10)), v4 = factor(rep(1:2, each = 50)), v5 = factor(rep(1:10, 10))) m1C <- lmer(v1~(1|v3) + (0+dummy(v4,"1")|v5), data = df3, control=lmerControl(check.nobs.vs.nlev="ignore", check.nobs.vs.nRE="ignore", check.conv.singular = "ignore")) expect_equal(head(predict(m1C, re.form = ~1|v3), 1), c(`1` = -0.035719520719991)) }) test_that("predict se.fit on response scale", { p1 <- suppressWarnings( predict(fit_cbpp_1, type = "link", se.fit = TRUE)) p2 <- suppressWarnings( predict(fit_cbpp_1, type = "response", se.fit = TRUE)) p3 <- suppressWarnings( predict(fit_cbpp_1, type = "response", newdata = cbpp, se.fit = TRUE)) expect_identical(p2$se.fit, p3$se.fit) expect_equal(p1$se.fit*binomial()$mu.eta(p1$fit), p2$se.fit) }) test_that("predictions work with se.fit and subset of grouping variable levels", { ## Idea: when predicting where the newdata has less groups than what ## the full model accounted for, Cmat needs to be subsetted to match ## the dimensions for cbind(Z, X) ## Code inspired: https://github.com/lme4/lme4/issues/866#issue-3358828496 set.seed(123) dat <- data.frame( outcome = rbinom(n = 100, size = 1, prob = 0.35), var_binom = as.factor(rbinom(n = 100, size = 1, prob = 0.7)), var_cont = rnorm(n = 100, mean = 10, sd = 7), grp = as.factor(sample(letters[1:4], size = 100, replace = TRUE)) ) m1 <- lme4::glmer( outcome ~ var_binom + var_cont + (1 | grp), data = dat, family = binomial(link = "logit") ) ## d <- insight::get_datagrid(m1, "var_binom", include_random = TRUE) d <- data.frame(var_binom = factor(0:1), var_cont = c(9.24717241397544, 9.24717241397544), grp = factor(c("a","b"), levels = letters[1:4])) pp <- suppressWarnings(predict(m1, newdata = d, se.fit = TRUE)) expect_equal(pp, list(fit = c(`1` = -0.4338277, `2` = -0.5993396), se.fit = c(`1` = 0.4255397, `2` = 0.2779372)), tol = 1e-6) d2 <- dat[sample(1:nrow(dat), size = 20),] d2 <- d2[!("c" == d2$grp), ] pp2 <- suppressWarnings(predict(m1, newdata = d2, se.fit = TRUE)) expect_identical(lengths(pp2), c(fit=16L, se.fit=16L)) expect_equal(lapply(pp2, head, 2), list(fit = c(`37` = -0.5109994, `29` = -0.5704263), se.fit = c(`37` = 0.5151070, `29` = 0.3258730)), tol = 1e-7) set.seed(123) dat2 <- expand.grid( grp.1 = factor(c("a.1", "b.1", "c.1", "d.1")), grp.2 = factor(c("a.1", "b.1", "c.1", "d.1")), rep = 1:10) dat2$y <- simulate(~ 1 + (1|grp.1) + (1|grp.2), family = binomial, newdata = dat2, newparams = list(beta = 0, theta = c(1, 1)))[[1]] m2 <- lme4::glmer( y ~ 1 + (1|grp.1) + (1|grp.2), data = dat2, family = binomial(link = "logit") ) dsub <- expand.grid( grp.1 = c("a.1", "b.1"), grp.2 = c("a.1", "b.1")) p1 <- suppressWarnings(predict(m2, newdata = dsub, se.fit = TRUE)) p2 <- suppressWarnings(predict(m2, se.fit = TRUE)) ss <- subset(dat2, grp.1 %in% c("a.1", "b.1") & grp.2 %in% c("a.1", "b.1")) w <- as.numeric(rownames(ss)[1:4]) expect_equal(lapply(p2, function(x) x[w]), p1, check.attributes = FALSE) ## Example: we have grouping variables g1 and g2 which each have levels a, b, c, d. ## This safeguards cases if we ask for levels a, b in g1 and b, c in g2 set.seed(1) dat3 <- expand.grid( g1 = factor(c("a", "b", "c", "d")), g2 = factor(c("a", "b", "c", "d")), rep = 1:10 ) dat3$y <- simulate(~ 1 + (1|g1) + (1|g2), family = binomial, newdata = dat3, newparams = list(beta = 0, theta = c(1, 1)))[[1]] m3 <- lme4::glmer( y ~ 1 + (1|g1) + (1|g2), data = dat3, family = binomial(link = "logit") ) dsub2 <- expand.grid( g1 = c("a", "b"), g2 = c("c", "d")) tp1 <- suppressWarnings(predict(m3, se.fit = TRUE)) tp2 <- suppressWarnings(predict(m3, newdata = dsub2, se.fit = TRUE)) ss2 <- subset(dat3, g1 %in% c("a", "b") & g2 %in% c("c", "d")) w2 <- as.numeric(rownames(ss2)[1:4]) expect_equal(lapply(tp1, function(x) x[w2]), tp2, check.attributes = FALSE) ## Case where there are multiple grouping variables set.seed(1) sleepstudy$Subject2 <- rep(1:5, each = 36) m5 <- lmer(Reaction ~ Days + (1 + Days | Subject) + (1 | Subject2), data = sleepstudy) d <- sleepstudy[sample(1:nrow(sleepstudy), size = 30), ] pms1 <- predict(m5, se.fit = TRUE, re.form = NULL, allow.new.levels = FALSE) pms2 <- predict(m5, newdata = d, se.fit = TRUE, re.form = NULL, allow.new.levels = FALSE) psw <- as.numeric(rownames(d)) expect_equal(lapply(pms1, function(x) x[psw]), pms2, check.attributes = FALSE) ## Case where we are adding new levels lm1 <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy) d373 <- data.frame(Days = 0, Subject = "373") pred373 <- predict(lm1, newdata = d373, se.fit = TRUE, re.form = NULL, allow.new.levels = TRUE) expected_373 <- fixef(lm1)["(Intercept)"] + fixef(lm1)["Days"] * d373$Days expect_equal(pred373$fit, expected_373, check.attributes = FALSE, tol = 10e-16) ## Extending previous example: multiple new levels for different groups dsub3 <- expand.grid( g1 = c("k", "m"), g2 = c("k", "m")) tp3 <- suppressWarnings(predict(m3, newdata = dsub3, se.fit = TRUE, allow.new.levels = TRUE)) expected_tp3 <- fixef(m3)["(Intercept)"] expect_true(all(tp3$fit == expected_tp3)) ## Case where user specified allow.new.levels = TRUE but ## but no new levels were actually added tp4 <- suppressWarnings(predict(m3, se.fit = TRUE, allow.new.levels = TRUE)) ## Ideally: predictions should remain the same as before. expect_equal(lapply(tp4, function(x) x[w2]), tp2, check.attributes = FALSE) ## Test that this also works under glmer. gm1 <- glmer(round(Reaction) ~ Days + (1 + Days | Subject), data = sleepstudy, family = poisson) d2 <- data.frame(Days = 0, Subject = "373") gp1 <- suppressWarnings(predict(gm1, newdata = d2, se.fit = TRUE, re.form = NULL, allow.new.levels = TRUE)) expected_gp1 <- fixef(gm1)["(Intercept)"] expect_true(all(gp1$fit == expected_gp1)) }) lme4/tests/testthat/test-summary.R0000644000176200001440000001266415103764661016734 0ustar liggesuserstry(detach("package:lmerTest"), silent = TRUE) testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 #context("summarizing/printing models") test_that("lmer", { set.seed(0) J <- 8 n <- 10 N <- J * n beta <- c(5, 2, 4) u <- matrix(rnorm(J * 3), J, 3) x.1 <- rnorm(N) x.2 <- rnorm(N) g <- rep(1:J, rep(n, J)) y <- 1 * (beta[1] + u[g,1]) + x.1 * (beta[2] + u[g,2]) + x.2 * (beta[3] + u[g,3]) + rnorm(N) tmpf <- function(x) capture.output(print(summary(x),digits=1)) tfun <- function(cc) { w <- grep("Fixed effects:", cc) cc[w:length(cc)] } C1 <- lmerControl(optimizer="nloptwrap", optCtrl=list(xtol_abs=1e-6, ftol_abs=1e-6)) m1 <- lmer(y ~ x.1 + x.2 + (1 + x.1 | g), control=C1) m2 <- lmer(y ~ x.1 + x.2 + (1 + x.1 + x.2 | g), control=C1) cc1 <- tmpf(m1) cc2 <- tmpf(m2) ## FIXME: correlation of fixed effects printed inconsistently. ## If (1) LME4_TEST_LEVEL == 100 *and* after running all of prior ## tests, something (package load? options setting?) changes ## so that the fixed-effect correlations are no longer printed ## out, and this test fails ## would like to sort this out but realistically not sure it's worth it? t1 <- tfun(cc1) vv <- vcov(m1) ss <- sessionInfo() save(m1, vv, ss, file = sprintf("test-summary_testlevel_%d.rda", testLevel)) expect_equal(t1, c("Fixed effects:", " Estimate Std. Error t value", "(Intercept) 5.4 0.5 12", "x.1 1.9 0.4 5", "x.2 4.0 0.1 28", "", "Correlation of Fixed Effects:", " (Intr) x.1 ", "x.1 -0.019 ", "x.2 0.029 -0.043" )) expect_equal(tfun(cc2), c("Fixed effects:", " Estimate Std. Error t value", "(Intercept) 5.4 0.4 12", "x.1 2.0 0.4 5", "x.2 4.0 0.3 15", "", "Correlation of Fixed Effects:", " (Intr) x.1 ", "x.1 -0.069 ", "x.2 0.136 -0.103" )) }) ## Tests with regards to auto-scaling; making sure we get expected behaviours. set.seed(1) sleepstudy$var1 = runif(nrow(sleepstudy), 1e6, 1e7) form <- Reaction ~ var1 + Days + (Days | Subject) scf1 <- lmer(form, control = lmerControl(autoscale = TRUE), sleepstudy) scf2 <- suppressWarnings( update(scf1, control = lmerControl(autoscale = FALSE)) ) test_that("lmer(): ensuring we get the internal scale for X with getME", { res1 <- getME(scf1, "X")[180, ] valtest1 <- c(1, 1.456867279040504, 1.562340900990911) expect_equal(res1, valtest1, check.attributes = FALSE, tolerance =1e-10) }) test_that("lmer(): ensuring we get the internal scale for beta with getME", { res2 <- getME(scf1, "beta") valtest2 <- c(298.507891666666751, 1.621716753196637, 30.161580776828778) expect_equal(res2, valtest2, tolerance =1e-10) }) test_that("lmer(): model.matrix should provide unscaled version at default", { res3 <- model.matrix.merMod(scf1)[180, ] valtest3 <- c(1, 9127734.503475949, 9) expect_equal(res3, valtest3, check.attributes = FALSE, tolerance =1e-6) res4 <- model.matrix.merMod(scf1, noScale = TRUE)[180, ] valtest4 <- c(1, 1.45686727904050395, 1.5623409009909106) expect_equal(res4, valtest4, check.attributes = FALSE, tolerance =1e-6) }) test_that("lmer(): fixef.merMod() should provide unscaled version at default", { res5 <- fixef.merMod(scf1) valtest5 <- fixef.merMod(scf2) expect_equal(res5, valtest5, check.attributes = FALSE, tolerance =1e-10) res6 <- fixef.merMod(scf1, noScale = TRUE) valtest6 <- c(298.507891666666751, 1.621716753196637, 30.161580776828778) expect_equal(res6, valtest6, check.attributes = FALSE, tolerance =1e-10) }) test_that("lmer(): vcov.merMod() should provide unscaled version at default", { res7 <- vcov.merMod(scf1) valtest7 <- vcov.merMod(scf2) expect_true(all.equal(res7, valtest7, check.attributes = FALSE, tolerance =1e-6)) res8 <- vcov.merMod(scf1, noScale = TRUE) valtest8 <- as( matrix(c(8.12278783645280e+01, -3.24525171154812e-15, 26.5884231100835215, -3.24525171154812e-15, 4.39230297872591e+00, 0.0344711252408276, 2.65884231100835e+01, 3.44711252408276e-02, 19.7018459306493092), nrow = 3, byrow = TRUE), "dpoMatrix") expect_true(all.equal(res8, valtest8, check.attributes = FALSE, tolerance =1e-10)) }) ## Doing autoscaling tests with glmer instead. set.seed(1) cbpp$var1 = runif(nrow(cbpp), 1e3, 1e5) form <- cbind(incidence, size - incidence) ~ var1 + period + (1 | herd) gfit1 <- glmer(form, control = glmerControl(autoscale = TRUE), data = cbpp, family = binomial) gfit2 <- suppressWarnings( update(gfit1, control = glmerControl(autoscale = FALSE)) ) test_that("glmer(): back transform matches similar model", { gres1 <- fixef.merMod(gfit1) og1 <- fixef.merMod(gfit2) expect_equal(gres1, og1, tolerance = 1e-5) gres2 <- vcov.merMod(gfit1) og2 <- suppressWarnings(vcov.merMod(gfit2)) expect_true(all.equal(gres2, og2, check.attributes = FALSE, tolerance = 0.04)) }) lme4/tests/predsim.R0000644000176200001440000000460215022107260014040 0ustar liggesusers## compare range, average, etc. of simulations to ## conditional and unconditional prediction library(lme4) do.plot <- FALSE if (.Platform$OS.type != "windows") { ## use old (<=3.5.2) sample() algorithm if necessary if ("sample.kind" %in% names(formals(RNGkind))) { suppressWarnings(RNGkind("Mersenne-Twister", "Inversion", "Rounding")) } fm1 <- lmer(Reaction~Days+(1|Subject),sleepstudy) set.seed(101) pp <- predict(fm1) rr <- range(usim2 <- simulate(fm1,1,use.u=TRUE)[[1]]) stopifnot(all.equal(rr,c(159.3896,439.1616),tolerance=1e-6)) if (do.plot) { plot(pp,ylim=rr) lines(sleepstudy$Reaction) points(simulate(fm1,1)[[1]],col=4) points(usim2,col=2) } set.seed(101) ## conditional prediction ss <- simulate(fm1,1000,use.u=TRUE) ss_sum <- t(apply(ss,1,quantile,c(0.025,0.5,0.975))) plot(pp) matlines(ss_sum,col=c(1,2,1),lty=c(2,1,2)) stopifnot(all.equal(ss_sum[,2],pp,tolerance=5e-3)) ## population-level prediction pp2 <- predict(fm1, re.form=NA) ss2 <- simulate(fm1,1000,use.u=FALSE) ss_sum2 <- t(apply(ss2,1,quantile,c(0.025,0.5,0.975))) if (do.plot) { plot(pp2,ylim=c(200,400)) matlines(ss_sum2,col=c(1,2,1),lty=c(2,1,2)) } stopifnot(all.equal(ss_sum2[,2],pp2,tolerance=8e-3)) ## predict(...,newdata=...) on models with derived variables in the random effects ## e.g. (f:g, f/g) set.seed(101) d <- expand.grid(f=factor(letters[1:10]),g=factor(letters[1:10]), rep=1:10) d$y <- rnorm(nrow(d)) m1 <- lmer(y~(1|f:g),d) p1A <- predict(m1) p1B <- predict(m1,newdata=d) stopifnot(all.equal(p1A,p1B)) m2 <- lmer(y~(1|f/g),d) p2A <- predict(m2) p2B <- predict(m2,newdata=d) stopifnot(all.equal(p2A,p2B)) ## with numeric grouping variables dn <- transform(d,f=as.numeric(f),g=as.numeric(g)) m1N <- update(m1,data=dn) p1NA <- predict(m1N) p1NB <- predict(m1N,newdata=dn) stopifnot(all.equal(p1NA,p1NB)) ## simulate with modified parameters set.seed(1) s1 <- simulate(fm1) set.seed(1) s2 <- simulate(fm1,newdata=model.frame(fm1), newparams=getME(fm1,c("theta","beta","sigma"))) all.equal(s1,s2) fm0 <- update(fm1,.~.-Days) ## ## sim() -> simulate() -> refit() -> deviance ## ## predictions and simulations with offsets set.seed(101) d <- data.frame(y=rpois(100,5),x=rlnorm(100,1,1), f=factor(sample(10,size=100,replace=TRUE))) gm1 <- glmer(y~offset(log(x))+(1|f),data=d, family=poisson) s1 <- simulate(gm1) } ## skip on windows (for speed) lme4/tests/nlmer-conv.R0000644000176200001440000000171215022107260014454 0ustar liggesusers### nlmer() convergence testing / monitoring / ... ## ------------------- ### The output of tests here are *not* 'diff'ed (<==> no *.Rout.save file) library(lme4) ## 'Theoph' Data modeling if (lme4:::testLevel() > 1) { Th.start <- c(lKe=-2.5, lKa=0.5, lCl=-3) (nm2 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ lKe + lKa + lCl|Subject, Theoph, start = Th.start)) (nm3 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKe|Subject) + (lKa|Subject) + (lCl|Subject), Theoph, start = Th.start)) ## dropping lKe from random effects: (nm4 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ lKa + lCl|Subject, Theoph, start = Th.start, control = nlmerControl(tolPwrss=1e-8))) (nm5 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKa|Subject) + (lCl|Subject), Theoph, start = Th.start)) } lme4/tests/lmer2_ex.R0000644000176200001440000000564314677066752014151 0ustar liggesusersstopifnot(suppressPackageStartupMessages(require(lme4))) ## Using simple generated data -- fully balanced here, unbalanced later set.seed(1) dat <- within(data.frame(lagoon = factor(rep(1:4, each = 25)), habitat = factor(rep(1:20, each = 5))), { ## a simple lagoon effect but no random effect y <- round(10*rnorm(100, m = 10*as.numeric(lagoon))) ## Here, *with* an RE, sigma_a = 100 RE <- rep(round(rnorm(nlevels(habitat), sd = 100)), each = 5) y2 <- y + RE }) ## FIXME: want lmer(* , sparseX = TRUE ) {as in lme4a} if (FALSE) { # need to adapt to new structure ##' ##' ##'
##' @title Comparing the different versions of lmer() for same data & model ##' @param form ##' @param data ##' @param verbose ##' @return chkLmers <- function(form, data, verbose = FALSE, tol = 200e-7) # had tol = 7e-7 working .. { # m <- lmer1(form, data = data) # ok, and more clear # m. <- lmer1(form, data = data, sparseX = TRUE, verbose = verbose) m2 <- lmer (form, data = data, verbose = verbose) # lmem-dense m2. <- lmer (form, data = data, sparseX = TRUE, verbose = verbose) ## Eq <- function(x,y) all.equal(x,y, tolerance = tol) stopifnot(## Compare sparse & dense of the new class results identical(slotNames(m2), slotNames(m2.)) , identical(slotNames(m2@fe), slotNames(m2.@fe)) , Eq(m2@resp, m2.@resp) , Eq(m2@re, m2.@re) , Eq(m2@fe@coef, m2.@fe@coef) , ## and now compare with the "old" (class 'mer') # Eq(unname(fixef(m)), m2@fe@beta) # , # Eq(unname(fixef(m.)), m2.@fe@beta) # , ## to do ## all.equal(ranef(m)), m2@re) ## all.equal(ranef(m.)), m2.@re) TRUE) invisible(list(#m=m, m.=m., m2 = m2, m2. = m2.)) } chk1 <- chkLmers(y ~ 0+lagoon + (1|habitat), data = dat, verbose = TRUE) chk2 <- chkLmers(y2 ~ 0+lagoon + (1|habitat), data = dat, verbose = TRUE) chk1$m2 ## show( lmer() ) -- sigma_a == 0 chk2$m2. ## show( lmer( ) ) -- n <- nrow(dat) for(i in 1:20) { iOut <- sort(sample(n, 1+rpois(1, 3), replace=FALSE)) cat(i,": w/o ", paste(iOut, collapse=", ")," ") chkLmers(y ~ 0+lagoon + (1|habitat), data = dat[- iOut,]) chkLmers(y2 ~ lagoon + (1|habitat), data = dat[- iOut,]) cat("\n") } ## One (rare) example where the default tolerance is not sufficient: dat. <- dat[- c(14, 34, 66, 67, 71, 88),] try( chkLmers(y ~ 0+lagoon + (1|habitat), data = dat.) ) ## Error: Eq(unname(fixef(m)), m2@fe@beta) is not TRUE ## ## but higher tolerance works: chkLmers(y ~ 0+lagoon + (1|habitat), data = dat., tol = 2e-4, verbose=TRUE) } proc.time() sessionInfo() lme4/tests/lmer-conv.R0000644000176200001440000000133215022107260014274 0ustar liggesusersif (lme4:::testLevel() > 1 || .Platform$OS.type!="windows") { ### lmer() convergence testing / monitoring / ... ## ------------------ ### The output of tests here are *not* 'diff'ed (<==> no *.Rout.save file) library(lme4) ## convergence on boundary warnings load(system.file("external/test3comp.rda", package = "Matrix")) b3 <- lmer(Y3 ~ (1|Sample) + (1|Operator/Run), test3comp, verb = TRUE) if (isTRUE(try(data(Early, package = 'mlmRev')) == 'Early')) { Early$tos <- Early$age - 0.5 # time on study b1 <- lmer(cog ~ tos + trt:tos + (tos|id), Early, verb = TRUE) } cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' } ## skip on windows (for speed) lme4/tests/REMLdev.R0000644000176200001440000000315715022107260013637 0ustar liggesuserslibrary(lme4) ## show important current settings {for reference, etc} -- [early, and also on Windows !]: source(system.file("test-tools-1.R", package = "Matrix"), keep.source = FALSE) ## N.B. is.all.equal[34]() and assert.EQ() use 'tol', not 'tolerance' str( lmerControl()) str(glmerControl()) str(nlmerControl()) ls.str(environment(nloptwrap)) ## see Details under ?deviance.merMod: fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) fm1ML <- refitML(fm1) REMLcrit(fm1) ## 1743.628 deviance(fm1ML) ## 1751.939 deviance(fm1,REML=FALSE) ## FIXME: not working yet (NA) deviance(fm1,REML=TRUE) ## from lme4.0 oldvals <- c(REML=1743.6282722424, ML=1751.98581103058) ## leave out ML values for REML fits for now ... stopifnot( is.all.equal3(REMLcrit(fm1), deviance(fm1,REML=TRUE), deviance(fm1ML,REML=TRUE),oldvals["REML"]), all.equal(deviance(fm1ML),deviance(fm1ML,REML=FALSE),oldvals["ML"]), all.equal(REMLcrit(fm1)/-2,c(logLik(fm1)),c(logLik(fm1ML,REML=TRUE)),c(logLik(fm1,REML=TRUE))), all.equal(deviance(fm1ML)/-2,c(logLik(fm1ML,REML=FALSE)), c(logLik(fm1ML,REML=FALSE)))) ## should be: ## stopifnot( ## all.equal(deviance(fm1),deviance(fm1,REML=TRUE),deviance(fm1ML,REML=TRUE),oldvals["REML"]), ## all.equal(deviance(fm1ML),deviance(fm1,REML=FALSE),deviance(fm1ML,REML=FALSE),oldvals["ML"]), ## all.equal(deviance(fm1)/2,c(logLik(fm1)),c(logLik(fm1ML,REML=TRUE)),c(logLik(fm1,REML=TRUE))), ## all.equal(deviance(fm1ML)/2,c(logLik(fm1,REML=FALSE)),c(logLik(fm1ML,REML=FALSE)), ## c(logLik(fm1ML,REML=FALSE)))) lme4/tests/agridat_gotway.R0000644000176200001440000000462315022107260015405 0ustar liggesusers## require(agridat) ## dat <- gotway.hessianfly if (.Platform$OS.type != "windows") { ## don't actually use gotway_hessianfly_fit or gotway_hessianfly_prof, ## so we should be OK even with R< 3.0.1 load(system.file("testdata","gotway_hessianfly.rda",package="lme4")) # Block random. See Glimmix manual, output 1.18. # Note: (Different parameterization) ## require("lme4.0") ## fit2 <- glmer(cbind(y, n-y) ~ gen + (1|block), data=dat, family=binomial) ## params <- list(fixef=fixef(fit2),theta=getME(fit2,"theta")) ## detach("package:lme4.0") lme4.0fit <- structure(list(fixef = structure(c(1.50345713031203, -0.193853259383803, -0.540808391060274, -1.43419379979154, -0.203701042949808, -0.978322555343941, -0.604078624475678, -1.67742449813309, -1.39842466673692, -0.681709344788684, -1.46295367186169, -1.45908310198959, -3.55285756517073, -2.50731975980307, -2.08716296677356, -2.96974270029992), .Names = c("(Intercept)", "genG02", "genG03", "genG04", "genG05", "genG06", "genG07", "genG08", "genG09", "genG10", "genG11", "genG12", "genG13", "genG14", "genG15", "genG16")), theta = structure(0.0319087494293615, .Names = "block.(Intercept)")), .Names = c("fixef", "theta")) ## start doesn't work because we don't get there library(lme4) m1 <- glmer(cbind(y, n-y) ~ gen + (1|block), data=gotway.hessianfly, family=binomial) m1B <- update(m1,control=glmerControl(optimizer="bobyqa")) max(abs(m1@optinfo$derivs$gradient)) ## 0.0012 max(abs(m1B@optinfo$derivs$gradient)) ## 2.03e-5 abs(m1@optinfo$derivs$gradient)/abs(m1B@optinfo$derivs$gradient) ## bobyqa gets gradients *at least* 1.64* lower lme4fit <- list(fixef=fixef(m1),theta=getME(m1,"theta")) ## hack around slight naming differences lme4fit$theta <- unname(lme4fit$theta) lme4.0fit$theta <- unname(lme4.0fit$theta) ## difference in theta on x86_64-w64-mingw32 (64-bit) with r-devel is 0.000469576 stopifnot(all.equal(lme4fit, lme4.0fit, tolerance = 5e-4)) ## Fun stuff: visualize and alternative model ## library(ggplot2) ## dat$prop <- dat$y/dat$n ## theme_set(theme_bw()) ## ggplot(dat,aes(x=gen,y=prop,colour=block))+geom_point(aes(size=n))+ ## geom_line(aes(group=block,colour=block))+ ## geom_smooth(family=binomial,aes(weight=n,colour=block,group=block),method="glm", ## alpha=0.1) ## dat$obs <- factor(seq(nrow(dat))) ## m2 <- glmer(cbind(y, n-y) ~ block+ (1|gen) + (1|obs), data=dat, family=binomial) } ## not on windows/CRAN lme4/tests/modFormula.R0000644000176200001440000000501115022107260014475 0ustar liggesusersif (.Platform$OS.type != "windows") { library(lme4) library(testthat) .get.checkingOpts <- lme4:::.get.checkingOpts stopifnot(identical( .get.checkingOpts( c("CheckMe", "check.foo", "check.conv.1", "check.rankZ", "check.rankX")) , c("check.foo", "check.rankZ"))) lmod <- lFormula(Reaction ~ Days + (Days|Subject), sleepstudy) devfun <- do.call(mkLmerDevfun, lmod) opt <- optimizeLmer(devfun) cc <- lme4:::checkConv(attr(opt,"derivs"), opt$par, ctrl = lmerControl()$checkConv, lbound=environment(devfun)$lower) fm1 <- mkMerMod(environment(devfun), opt, lmod$reTrms, fr = lmod$fr, lme4conv=cc) fm2 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ## basic equivalence fm1C <- fm1 fm1C@call <- fm2@call expect_equal(fm2,fm1C) expect_equal(range(residuals(fm1)), c(-101.18, 132.547), tolerance = 1e-5) # these are "outliers"! expect_is(model.frame(fm1),"data.frame") ## formulae mfm1 <- model.frame(fm1) expect_equal(formula(fm1), Reaction ~ Days + (Days | Subject)) expect_equal(formula(terms(mfm1)), Reaction ~ Days + (Days + Subject)) new_form_modframe <- (getRversion() >= "3.6.0" && as.numeric(version[["svn rev"]]) >= 75891) expect_equal(formula(mfm1), if(new_form_modframe) { Reaction ~ Days + (Days + Subject) } else Reaction ~ Days + Subject ) ## predictions expect_equal(predict(fm1,newdata=sleepstudy[1:10,],re.form=NULL), predict(fm2,newdata=sleepstudy[1:10,],re.form=NULL)) expect_equal(predict(fm1,newdata=sleepstudy), predict(fm1)) lmodOff <- lFormula(Reaction ~ Days + (Days|Subject) + offset(0.5*Days), sleepstudy) devfunOff <- do.call(mkLmerDevfun, lmodOff) opt <- optimizeLmer(devfunOff) fm1Off <- mkMerMod(environment(devfunOff), opt, lmodOff$reTrms, fr = lmodOff$fr) fm2Off <- lmer(Reaction ~ Days + (Days|Subject) + offset(0.5*Days), sleepstudy) expect_equal(predict(fm1Off,newdata=sleepstudy[1:10,],re.form=NULL), predict(fm2Off,newdata=sleepstudy[1:10,],re.form=NULL)) ## FIXME: need more torture tests with offset specified, in different environments ... ## FIXME: drop1(.) doesn't work with modular objects ... hard to see how it ## could, though ... ## drop1(fm1Off) drop1(fm2Off) } ## skip on windows (for speed) lme4/tests/drop1contrasts.R0000644000176200001440000000144415022107260015364 0ustar liggesusers## drop1 may not work right with contrasts: make up an example something like this ... ## options(contrasts=c("contr.sum","contr.poly")) ## drop1(fecpoiss_lm3,test="Chisq",scope=.~.) if (.Platform$OS.type != "windows") withAutoprint({ library(lme4) oldopts <- options(contrasts=c("contr.sum","contr.poly")) fm1 <- lmer(Reaction~Days+(Days|Subject),data=sleepstudy) drop1(fm1,test="Chisq") ## debug(lme4:::drop1.merMod) drop1(fm1,test="Chisq",scope=.~.) fm0 <- lm(Reaction~Days+Subject,data=sleepstudy) drop1(fm0,test="Chisq",scope=.~.) options(oldopts) ## restore original contrasts ff <- function() { lmer(Reaction~Days+(Days|Subject),data=sleepstudy) } drop1(ff()) ## OK because sleepstudy is accessible! }) ## skip on windows (for speed) lme4/tests/priorWeights.R0000644000176200001440000001340715076743456015114 0ustar liggesusers## use old (<=3.5.2) sample() algorithm if necessary if ("sample.kind" %in% names(formals(RNGkind))) { suppressWarnings(RNGkind("Mersenne-Twister", "Inversion", "Rounding")) } compFunc <- function(lmeMod, lmerMod, tol = 1e-2){ lmeVarCorr <- nlme:::VarCorr(lmeMod)[,"StdDev"] lmeCoef <- summary(lmeMod)$tTable[,-c(3,5)] lmeOut <- c(as.numeric(lmeVarCorr), as.numeric(lmeCoef)) keep <- !is.na(lmeOut) lmeOut <- lmeOut[keep] dn <- dimnames(lmeCoef) if(is.null(dn)) dn <- list("", names(lmeCoef)) names(lmeOut) <- c( paste(names(lmeVarCorr), "Var"), as.character(do.call(outer, c(dn, list("paste")))))[keep] ## get nested RE variances in the same order as nlme ## FIXME: not sure if this works generally vcLmer <- VarCorr(lmerMod) vcLmer <- vcLmer[length(vcLmer):1] ## lmerVarCorr <- c(sapply(vcLmer, attr, "stddev"), attr(VarCorr(lmerMod), "sc")) ## differentiate lme4{new} and lme4.0 : lmerCoef <- if(is(lmerMod, "merMod")) summary(lmerMod)$coefficients else summary(lmerMod)@coefs lmerOut <- c(lmerVarCorr, as.numeric(lmerCoef)) names(lmerOut) <- names(lmeOut) return(list(target = lmeOut, current = lmerOut, tolerance = tol)) } if (.Platform$OS.type != "windows") { set.seed(1) nGroups <- 100 nObs <- 1000 # explanatory variable with a fixed effect explVar1 <- rnorm(nObs) explVar2 <- rnorm(nObs) # random intercept among levels of a grouping factor groupFac <- as.factor(rep(1:nGroups,each=nObs/nGroups)) randEff0 <- rep(rnorm(nGroups),each=nObs/nGroups) randEff1 <- rep(rnorm(nGroups),each=nObs/nGroups) randEff2 <- rep(rnorm(nGroups),each=nObs/nGroups) # residuals with heterogeneous variance residSD <- rpois(nObs,1) + 1 residError <- rnorm(nObs,sd=residSD) # response variable respVar <- randEff0 + (1+randEff1)*explVar1 + (1+randEff2)*explVar2 + residError # rename to fit models on one line y <- respVar x <- explVar1 z <- explVar2 g <- groupFac v <- residSD^2 w <- 1/v library("nlme") lmeMods <- list( ML1 = lme(y ~ x, random = ~ 1|g, weights = varFixed(~v), method = "ML"), REML1 = lme(y ~ x, random = ~ 1|g, weights = varFixed(~v), method = "REML"), ML2 = lme(y ~ x, random = ~ x|g, weights = varFixed(~v), method = "ML"), REML2 = lme(y ~ x, random = ~ x|g, weights = varFixed(~v), method = "REML"), ML1 = lme(y ~ x+z, random = ~ x+z|g, weights = varFixed(~v), method = "ML"), REML2 = lme(y ~ x+z, random = ~ x+z|g, weights = varFixed(~v), method = "REML")) library("lme4") lmerMods <- list( ML1 = lmer(y ~ x + (1|g), weights = w, REML = FALSE), REML1 = lmer(y ~ x + (1|g), weights = w, REML = TRUE), ML2 = lmer(y ~ x + (x|g), weights = w, REML = FALSE), REML2 = lmer(y ~ x + (x|g), weights = w, REML = TRUE), ML3 = lmer(y ~ x + z + (x+z|g), weights = w, REML = FALSE), REML3 = lmer(y ~ x + z + (x+z|g), weights = w, REML = TRUE)) comp <- mapply(compFunc, lmeMods, lmerMods, SIMPLIFY=FALSE) stopifnot(all(sapply(comp, do.call, what = all.equal))) ## Look at the relative differences: sapply(mapply(compFunc, lmeMods, lmerMods, SIMPLIFY=FALSE, tol = 0), do.call, what = all.equal) ## add simulated weights to the sleepstudy example n <- nrow(sleepstudy) v <- rpois(n,1) + 1 w <- 1/v sleepLme <- lme(Reaction ~ Days, random = ~ Days|Subject, sleepstudy, weights = varFixed(~v), method = "ML") sleepLmer <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, weights = w, REML = FALSE) sleepComp <- compFunc(sleepLme, sleepLmer) stopifnot(do.call(all.equal, sleepComp)) ## look at relative differences: sleepComp$tolerance <- 0 do.call(all.equal, sleepComp) if (require("mlmRev")) { n <- nrow(Chem97) v <- rpois(n,1) + 1 w <- 1/v Chem97Lme <- lme(score ~ 1, random = ~ 1|lea/school, Chem97) Chem97Lmer <- lmer(score ~ (1|lea/school), Chem97) Chem97Comp <- compFunc(Chem97Lme, Chem97Lmer) stopifnot(do.call(all.equal, Chem97Comp)) ## look at relative differences: Chem97Comp$tolerance <- 0 do.call(all.equal, Chem97Comp) } set.seed(2) n <- 40 w <- runif(n) x <- runif(n) g <- factor(sample(1:10,n,replace=TRUE)) Z <- model.matrix(~g-1); y <- Z%*%rnorm(ncol(Z)) + x + rnorm(n)/w^.5 m <- lmer(y ~ x + (1|g), weights=w, REML = TRUE) ## CRAN-forbidden: ## has4.0 <- require("lme4.0")) has4.0 <- FALSE if(has4.0) { ## m.0 <- lme4.0::lmer(y ~ x + (1|g), weights=w, REML = TRUE) lmer0 <- get("lmer", envir=asNamespace("lme4.0")) m.0 <- lmer0(y ~ x + (1|g), weights=w, REML = TRUE) dput(fixef(m.0)) # c(-0.73065400610675, 2.02895402562926) dput(sigma(m.0)) # 1.73614301673377 dput(VarCorr(m.0)$g[1,1]) # 2.35670451590395 dput(unname(coef(summary(m.0))[,"Std. Error"])) ## c(0.95070076853232, 1.37650858268602) } fixef_lme4.0 <- c(-0.7306540061, 2.0289540256) sigma_lme4.0 <- 1.7361430 Sigma_lme4.0 <- 2.3567045 SE_lme4.0 <- c(0.95070077, 1.37650858) if(has4.0) try(detach("package:lme4.0")) stopifnot(all.equal(unname(fixef(m)), fixef_lme4.0, tolerance = 1e-3)) all.equal(unname(fixef(m)), fixef_lme4.0, tolerance = 0) #-> 1.657e-5 ## but these are not at all equal : (all.equal(sigma(m), sigma_lme4.0, tolerance = 10^-3)) # 0.4276 (all.equal(as.vector(VarCorr(m)$g), Sigma_lme4.0, tolerance = 10^-3)) # 1.038 (all.equal(as.vector(summary(m)$coefficients[,2]), SE_lme4.0, tolerance = 10^-3)) # 0.4276 ## so, lme4.0 was clearly wrong here ##' make sure models that differ only in a constant ##' prior weight have identical deviance: fm <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy,REML=FALSE) fm_wt <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, weights = rep(5, nrow(sleepstudy)),REML=FALSE) all.equal(deviance(fm), deviance(fm_wt)) } ## skip 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lme4/R/0000755000176200001440000000000015113144725011317 5ustar liggesuserslme4/R/nbinom.R0000644000176200001440000001401515022107260012715 0ustar liggesusers##' @importFrom MASS negative.binomial ##' @importFrom MASS theta.ml ## ==> user should use getME(object, "glmer.nb.theta") getNBdisp <- function(object) environment(object@resp$family$aic)[[".Theta"]] ## Hidden, originally used at least once, well tested (!), but if(FALSE) # not needed anymore currently getNBdisp.fam <- function(familyString) as.numeric(sub(".*([-+]?\\d+(\\.\\d*)?([Ee][-+]?\\d+)?){1}.*", "\\1", familyString)) ## Package "constants" {only on depending the glmResp definition in ./AllClass.R}: glmResp.f.nms <- names(glmResp$fields()) glmNB.to.change <- setdiff(glmResp.f.nms, c("Ptr", "family")) ##' setNBdisp(object,theta) := ##' NB-object with changed [DISP]ersion parameter 'theta' (and all that entails) setNBdisp <- function(object,theta) { ## assign(".Theta",theta,envir=environment(object@resp$family$aic)) rr <- object@resp newresp <- do.call(glmResp$new, c(lapply(setNames(nm=glmNB.to.change), rr$field), list(family = MASS::negative.binomial(theta=theta)))) newresp$setOffset(rr$offset) newresp$updateMu(rr$eta - rr$offset) object@resp <- newresp object } refitNB <- function(object, theta, control = NULL) { refit(setNBdisp(object, theta), control = control) } ##' @title Optimize over the Negative Binomial Parameter Theta ##' @param object a "glmerMod" object, updated from poisson to negative.binomial() ##' @param interval and ##' @param tol are both passed to \code{\link{optimize}()}. ##' @param verbose ## show our own progress ##' @param control passed to \code{\link{refit}()} ##' @return the last fit, an object like 'object' optTheta <- function(object, interval = c(-5,5), tol = .Machine$double.eps^0.25, verbose = FALSE, control = NULL) { lastfit <- object it <- 0L NBfun <- function(t) { ## Kluge to retain last value and evaluation count {good enough for ..} f_refitNB <- factory(refitNB,types=c("message","warning")) lastfit <<- f_refitNB(lastfit, theta = exp(t), control = control) dev <- -2*logLik(lastfit) it <<- it+1L if (verbose) cat(sprintf("%2d: th=%#15.10g, dev=%#14.8f, beta[1]=%#14.8f\n", it, exp(t), dev, lastfit@beta[1])) dev } optval <- optimize(NBfun, interval=interval, tol=tol) stopifnot(all.equal(optval$minimum, log(getNBdisp(lastfit)))) ## FIXME: return eval count info somewhere else? MM: new slot there, why not? attr(lastfit,"nevals") <- it ## fix up the 'th' expression, replacing it by the real number, ## so effects:::mer.to.glm() can eval() it: lastfit@call$family[["theta"]] <- exp(optval$minimum) ## output warnings/messages from last fit for (m in c("warning","message")) { if (!is.null(x <- attr(lastfit,paste0("factory-",m)))) { for (i in x) { get(m,pos="package:base")(i) } } } lastfit } ## use MASS machinery to estimate theta from residuals est_theta <- function(object, limit = 20, eps = .Machine$double.eps^0.25, trace = 0) { Y <- model.response(model.frame(object)) ## may have NA values if na.exclude was used ... mu <- na.omit(fitted(object)) theta.ml(Y, mu, weights = object@resp$weights, limit = limit, eps = eps, trace = trace) } ## -------> ../man/glmer.Rd ##' glmer() for Negative Binomial ##' @param ... formula, data, etc: the arguments for ##' \code{\link{glmer}(..)} (apart from \code{family}!). glmer.nb <- function(..., interval = log(th) + c(-3,3), tol = 5e-5, verbose = FALSE, nb.control = NULL, initCtrl = list(limit = 20, eps = 2*tol, trace = verbose, theta = NULL)) { ## ?? E() is a placeholder; [-1] removes it ## left with a 'headless' list of elements in ... dotE <- as.list(substitute(E(...))[-1]) ## nE <- names(dotE <- as.list(substitute(E(...))[-1])) ## i <- match(c("formula",""), nE) ## i.frml <- i[!is.na(i)][[1]] # the index of "formula" in '...' ## dots <- list(...) mc <- match.call() if (is.null(th <- initCtrl$theta)) { mc[[1]] <- quote(lme4::glmer) mc$family <- quote(stats::poisson) mc$verbose <- (verbose>=2) mc$nb.control <- NULL ## ** FIXME: specifically add check.conv.singular="ignore"? ## suppress other warnings unless explicitly specified? g0 <- suppressMessages( eval(mc, parent.frame(1L)) ) th <- est_theta(g0, limit = initCtrl$limit, eps = initCtrl$eps, trace = initCtrl$trace) ## using format() on purpose, influenced by options(digits = *) : if(verbose) cat("th := est_theta(glmer(..)) =", format(th)) } mc$initCtrl <- NULL ## clear to prevent infinite recursion ## in initCtrl$theta reference above ... mc$family <- bquote(MASS::negative.binomial(theta=.(th))) ## ** see FIXME above g1 <- suppressMessages( eval(mc, parent.frame(1L)) ) if(verbose) cat(" --> dev.= -2*logLik(.) =", format(-2*logLik(g1)),"\n") ## fix the 'data' part (only now!) if("data" %in% names(g1@call)) { if (!is.null(dotE[["data"]])) { g1@call[["data"]] <- dotE[["data"]] } } else warning("no 'data = *' in glmer.nb() call ... Not much is guaranteed") other.args <- c("verbose","control") for (a in other.args) { if (a %in% names(g1@call)) { g1@call[[a]] <- dotE[[a]] } } ## FIXME: optTheta should also work by modifying mc directly, ## then re-evaluating, not via refit() ... control <- eval.parent(g1@call$control) res <- optTheta(g1, interval=interval, tol=tol, verbose=verbose, control = c(control, nb.control)) res } ## do we want to facilitate profiling on theta?? ## save evaluations used in optimize() fit? ## ('memoise'?) ## Again, I think that a reference class object would be a better approach. lme4/R/plot.R0000644000176200001440000004756115103163201012422 0ustar liggesusers## copied/modified from nlme ##' split, on the nm call, the rhs of a formula into a list of subformulas splitFormula <- function(form, sep = "/") { if (inherits(form, "formula") || mode(form) == "call" && form[[1]] == as.name("~")) splitFormula(form[[length(form)]], sep = sep) else if (mode(form) == "call" && form[[1]] == as.name(sep)) do.call(c, lapply(as.list(form[-1]), splitFormula, sep = sep)) else if (mode(form) == "(") splitFormula(form[[2]], sep = sep) else if (length(form)) list(asOneSidedFormula(form)) ## else ## NULL } ## Recursive version of all.vars allVarsRec <- function(object) { if (is.list(object)) { unlist(lapply(object, allVarsRec)) } else { all.vars(object) } } ## simple version of getData.gnls from nlme ## but we *should* and *can* work with environment(formula(.)) getData.merMod <- function(object) { mCall <- getCall(object) data <- eval(mCall$data, environment(formula(object))) if (!is.data.frame(data) && !is.matrix(data)) stop(paste(sQuote("data"),"object found is not a data frame or matrix")) return(data) } asOneFormula <- ## Constructs a linear formula with all the variables used in a ## list of formulas, except for the names in omit function(..., omit = c(".", "pi")) { names <- unique(allVarsRec(list(...))) names <- names[is.na(match(names, omit))] if (length(names)) as.formula(paste("~", paste(names, collapse = "+"))) # else NULL } getIDLabels <- function(object, form=formula(object)) { mf <- factorize(form,model.frame(object)) if (length(ff <- reformulas::findbars(form))>0) { grps <- lapply(ff,"[[",3) } else { grps <- form[[2]] } if (identical(grps,quote(.obs))) return(seq(fitted(object))) fList <- lapply(grps,function(x) eval(x,mf)) do.call(interaction,fList) } ## TESTING ## lme4:::getIDLabels(fm1) ## Return the formula(s) for the groups associated with object. ## The result is a one-sided formula unless asList is TRUE in which case ## it is a list of formulas, one for each level. getGroupsFormula <- function(object, asList = FALSE, sep = "+") UseMethod("getGroupsFormula") getGroupsFormula.default <- ## Return the formula(s) for the groups associated with object. ## The result is a one-sided formula unless asList is TRUE in which case ## it is a list of formulas, one for each level. function(object, asList = FALSE, sep = "/") { form <- formula(object) if (!inherits(form, "formula")){ stop("\"Form\" argument must be a formula") } form <- form[[length(form)]] if (!((length(form) == 3) && (form[[1]] == as.name("|")))) { ## no conditioning expression return(NULL) } ## val <- list( asOneSidedFormula( form[[ 3 ]] ) ) val <- splitFormula(asOneSidedFormula(form[[3]]), sep = sep) names(val) <- unlist(lapply(val, function(el) deparse(el[[2]]))) # if (!missing(level)) { # if (length(level) == 1) { # return(val[[level]]) # } else { # val <- val[level] # } # } if (asList) as.list(val) else as.formula(paste("~", paste(names(val), collapse = sep))) } getGroupsFormula.merMod <- function(object,asList=FALSE, sep="+") { if (asList) { lapply(names(object@flist),asOneSidedFormula) } else { asOneSidedFormula(paste(names(object@flist),collapse=sep)) } } getCovariateFormula <- function (object) { form <- formula(object) if (!(inherits(form, "formula"))) { stop("formula(object) must return a formula") } form <- form[[length(form)]] if (length(form) == 3 && form[[1]] == as.name("|")) { form <- form[[2]] } eval(substitute(~form)) } getResponseFormula <- function(object) { ## Return the response formula as a one sided formula form <- formula(object) if (!(inherits(form, "formula") && (length(form) == 3))) { stop("\"Form\" must be a two sided formula") } as.formula(paste("~", deparse(form[[2]]))) } ##' diagnostic plots for merMod fits ##' @param x a fitted [ng]lmer model ##' @param form an optional formula specifying the desired type of plot. Any ##' variable present in the original data frame used to obtain ##' \code{x} can be referenced. In addition, \code{x} itself can be ##' referenced in the formula using the symbol \code{"."}. Conditional ##' expressions on the right of a \code{|} operator can be used to ##' define separate panels in a lattice display. Default is ##' \code{resid(., type = "pearson") ~ fitted(.)}, corresponding to a plot ##' of the standardized residuals versus fitted values. ##' @param abline an optional numeric value, or numeric vector of length ##' two. If given as a single value, a horizontal line will be added to the ##' plot at that coordinate; else, if given as a vector, its values are ##' used as the intercept and slope for a line added to the plot. If ##' missing, no lines are added to the plot. ##' @param id an optional numeric value, or one-sided formula. If given as ##' a value, it is used as a significance level for a two-sided outlier ##' test for the standardized, or normalized residuals. Observations with ##' absolute standardized (normalized) residuals greater than the \eqn{1-value/2} ##' quantile of the standard normal distribution are ##' identified in the plot using \code{idLabels}. If given as a one-sided ##' formula, its right hand side must evaluate to a logical, integer, or ##' character vector which is used to identify observations in the ##' plot. If missing, no observations are identified. ##' @param idLabels an optional vector, or one-sided formula. If given as a ##' vector, it is converted to character and used to label the ##' observations identified according to \code{id}. If given as a ##' vector, it is converted to character and used to label the ##' observations identified according to \code{id}. If given as a ##' one-sided formula, its right hand side must evaluate to a vector ##' which is converted to character and used to label the identified ##' observations. Default is the interaction of all the grouping variables ##' in the data frame. The special formula ##' @param grid an optional logical value indicating whether a grid should ##' be added to plot. Default depends on the type of lattice plot used: ##' if \code{xyplot} defaults to \code{TRUE}, else defaults to ##' \code{FALSE}. ##' @param \dots optional arguments passed to the lattice plot function. ##' @details Diagnostic plots for the linear mixed-effects fit are obtained. The ##' \code{form} argument gives considerable flexibility in the type of ##' plot specification. A conditioning expression (on the right side of a ##' \code{|} operator) always implies that different panels are used for ##' each level of the conditioning factor, according to a lattice ##' display. If \code{form} is a one-sided formula, histograms of the ##' variable on the right hand side of the formula, before a \code{|} ##' operator, are displayed (the lattice function \code{histogram} is ##' used). If \code{form} is two-sided and both its left and ##' right hand side variables are numeric, scatter plots are displayed ##' (the lattice function \code{xyplot} is used). Finally, if \code{form} ##' is two-sided and its left had side variable is a factor, box-plots of ##' the right hand side variable by the levels of the left hand side ##' variable are displayed (the lattice function \code{bwplot} is used). ##' @author original version in \code{nlme} package by Jose Pinheiro and Douglas Bates ##' @examples ##' data(Orthodont,package="nlme") ##' fm1 <- lmer(distance ~ age + (age|Subject), data=Orthodont) ##' ## standardized residuals versus fitted values by gender ##' plot(fm1, resid(., scaled=TRUE) ~ fitted(.) | Sex, abline = 0) ##' ## box-plots of residuals by Subject ##' plot(fm1, Subject ~ resid(., scaled=TRUE)) ##' ## observed versus fitted values by Subject ##' plot(fm1, distance ~ fitted(.) | Subject, abline = c(0,1)) ##' ## residuals by age, separated by Subject ##' plot(fm1, resid(., scaled=TRUE) ~ age | Sex, abline = 0) ##' if (require(ggplot2)) { ##' ## we can create the same plots using ggplot2 and the fortify() function ##' fm1F <- fortify(fm1) ##' ggplot(fm1F, aes(.fitted,.resid)) + geom_point(colour="blue") + ##' facet_grid(.~Sex) + geom_hline(yintercept=0) ##' ## note: Subjects are ordered by mean distance ##' ggplot(fm1F, aes(Subject,.resid)) + geom_boxplot() + coord_flip() ##' ggplot(fm1F, aes(.fitted,distance))+ geom_point(colour="blue") + ##' facet_wrap(~Subject) +geom_abline(intercept=0,slope=1) ##' ggplot(fm1F, aes(age,.resid)) + geom_point(colour="blue") + facet_grid(.~Sex) + ##' geom_hline(yintercept=0)+geom_line(aes(group=Subject),alpha=0.4)+geom_smooth(method="loess") ##' ## (warnings about loess are due to having only 4 unique x values) ##' detach("package:ggplot2") ##' } ##' @S3method plot merMod ##' @method plot merMod ##' @export plot.merMod <- function(x, form = resid(., type = "pearson") ~ fitted(.), abline, id = NULL, idLabels = NULL, grid, ...) ## Diagnostic plots based on residuals and/or fitted values { object <- x if (!inherits(form, "formula")) stop("\"form\" must be a formula") ## constructing data ## can I get away with using object@frame??? allV <- all.vars(asOneFormula(form, id, idLabels)) allV <- allV[is.na(match(allV,c("T","F","TRUE","FALSE",".obs")))] if (length(allV) > 0) { data <- getData(object) if (is.null(data)) { # try to construct data alist <- lapply(as.list(allV), as.name) names(alist) <- allV alist <- c(list(as.name("data.frame")), alist) mode(alist) <- "call" data <- eval(alist, sys.parent(1)) } else if (any(naV <- is.na(match(allV, names(data))))) stop(allV[naV], " not found in data") } else data <- NULL ## this won't do because there may well be variables we want ## that were not in the model call ## data <- object@frame ## argument list dots <- list(...) args <- if (length(dots) > 0) dots else list() ## appending object to data, and adding observation-number variable if (length(data) > 0) { data <- cbind(data, .obs = seq(nrow(data))) } data <- as.list(c(as.list(data), . = list(object))) ## covariate - must always be present covF <- getCovariateFormula(form) .x <- eval(covF[[2]], data) if (!is.numeric(.x)) { stop("Covariate must be numeric") } argForm <- ~ .x argData <- data.frame(.x = .x, check.names = FALSE) if (is.null(args$xlab)) { if (is.null(xlab <- attr(.x, "label"))) xlab <- deparse(covF[[2]]) args$xlab <- xlab } ## response - need not be present respF <- getResponseFormula(form) if (!is.null(respF)) { .y <- eval(respF[[2]], data) if (is.null(args$ylab)) { if (is.null(ylab <- attr(.y, "label"))) ylab <- deparse(respF[[2]]) args$ylab <- ylab } argForm <- .y ~ .x argData[, ".y"] <- .y } ## groups - need not be present grpsF <- getGroupsFormula(form) if (!is.null(grpsF)) { ## ?? FIXME ??? gr <- splitFormula(grpsF, sep = "*") for(i in seq_along(gr)) { auxGr <- all.vars(gr[[i]]) for(j in auxGr) argData[[j]] <- eval(as.name(j), data) } argForm <- as.formula(paste(if (length(argForm) == 2) "~ .x |" else ".y ~ .x |", deparse(grpsF[[2]]))) } ## adding to args list args <- c(list(argForm, data = argData), args) if (is.null(args$strip)) { args$strip <- function(...) strip.default(..., style = 1) } if (is.null(args$cex)) args$cex <- par("cex") if (is.null(args$adj)) args$adj <- par("adj") if (!is.null(id)) { ## identify points in plot idResType <- "pearson" ## diff from plot.lme: 'normalized' not available id <- switch(mode(id), numeric = { if (id <= 0 || id >= 1) stop(shQuote("id")," must be between 0 and 1") abs(resid(object, type = idResType))/sigma(object) > -qnorm(id / 2) }, call = eval(asOneSidedFormula(id)[[2]], data), stop(shQuote("id")," can only be a formula or numeric.") ) if (is.null(idLabels)) { idLabels <- getIDLabels(object) } else { if (inherits(idLabels,"formula")) { idLabels <- getIDLabels(object,idLabels) } else if (is.vector(idLabels)) { if (length(idLabels <- unlist(idLabels)) != length(id)) { stop("\"idLabels\" of incorrect length") } } else stop("\"idLabels\" can only be a formula or a vector") } ## DON'T subscript by id, will be done later idLabels <- as.character(idLabels) } ## defining abline, if needed if (missing(abline)) { abline <- if (missing(form)) # r ~ f c(0, 0) else NULL } #assign("id", id , where = 1) #assign("idLabels", idLabels, where = 1) #assign("abl", abline, where = 1) assign("abl", abline) ## defining the type of plot if (length(argForm) == 3) { if (is.numeric(.y)) { # xyplot plotFun <- "xyplot" if (is.null(args$panel)) { args <- c(args, panel = list(function(x, y, subscripts, ...) { x <- as.numeric(x) y <- as.numeric(y) dots <- list(...) if (grid) panel.grid() panel.xyplot(x, y, ...) if (any(ids <- id[subscripts])){ ltext(x[ids], y[ids], idLabels[subscripts][ids], cex = dots$cex, adj = dots$adj) } if (!is.null(abl)) { if (length(abl) == 2) panel.abline(a = abl, ...) else panel.abline(h = abl, ...) } })) } } else { # assume factor or character plotFun <- "bwplot" if (is.null(args$panel)) { args <- c(args, panel = list(function(x, y, ...) { if (grid) panel.grid() panel.bwplot(x, y, ...) if (!is.null(abl)) { panel.abline(v = abl[1], ...) } })) } } } else { plotFun <- "histogram" if (is.null(args$panel)) { args <- c(args, panel = list(function(x, ...) { if (grid) panel.grid() panel.histogram(x, ...) if (!is.null(abl)) { panel.abline(v = abl[1], ...) } })) } } ## defining grid if (missing(grid)) { grid <- (plotFun == "xyplot") } # assign("grid", grid, where = 1) do.call(plotFun, as.list(args)) } ## no longer defining `fortify` S3 generic ##' @rdname fortify ##' @S3method fortify lmerMod ##' @method fortify lmerMod ##' @export ##' as function, not as S3 method, see ../man/fortify.Rd : fortify.merMod <- function(model, data=getData(model), ...) { ## FIXME: get influence measures via influence.ME? ## (expensive, induces dependency ...) ## FIXME: different kinds of residuals? ## FIXME: deal with na.omit/predict etc. data$.fitted <- predict(model) data$.resid <- resid(model) data$.scresid <- resid(model,type="pearson",scaled=TRUE) data } ## autoplot??? ## plot method for plot.summary.mer ... coefplot-style ## horizontal, vertical? other options??? ## scale? plot.summary.mer <- function(object, type="fixef", ...) { if(any(!type %in% c("fixef","vcov"))) stop("'type' not yet implemented: ", type) stop("FIXME -- not yet implemented") } ## TO DO: allow faceting formula ## TO DO: allow qqline to be optional ## TO DO (harder): steal machinery from qq.gam for better GLMM Q-Q plots qqmath.merMod <- function(x, data = NULL, id=NULL, idLabels=NULL, ...) { ## klugey attempt to detect whether user forgot to specify argument ## names explicitly (after addition of required 'data' argument) ## NOT completely tested! if (!is.null(data)) { idLabels <- id id <- data warning("qqmath.merMod takes ", sQuote("data"), "as its ", "first argument for S3 method compatibility: ", "in the future, please ", "specify the ", sQuote("id"), " and ", sQuote("idLabels"), " arguments explicitly ", "i.e. ", sQuote("qqmath(fitted_model, id = ..., [idLabels = ...], ...)")) } values <- residuals(x, type="pearson", scaled=TRUE) data <- getData(x) ## DRY: copied from plot.merMod, should modularize/refactor if (!is.null(id)) { ## identify points in plot id <- switch(mode(id), numeric = { if (id <= 0 || id >= 1) stop(shQuote("id")," must be between 0 and 1") as.logical(abs(values) > -qnorm(id / 2)) }, call = eval(asOneSidedFormula(id)[[2]], data), stop(shQuote("id")," can only be a formula or numeric.") ) if (is.null(idLabels)) { idLabels <- getIDLabels(x) } else { if (inherits(idLabels,"formula")) { idLabels <- getIDLabels(x,idLabels) } else if (is.vector(idLabels)) { if (length(idLabels <- unlist(idLabels)) != length(id)) { stop("\"idLabels\" of incorrect length") } } else stop("\"idLabels\" can only be a formula or a vector") } idLabels <- as.character(idLabels) } ## DON'T subscript by id, will be done later qqpanel <- function(x, subscripts, ...) { dots <- list(...) panel.qqmathline(x, ...) panel.qqmath(x, ...) if (any(ids <- id[subscripts])) { xs <- x[subscripts] pp <- setNames(ppoints(length(xs)), names(sort(xs))) ## want to plot qnorm(pp) vs sort(x) ## ... but want to pick out the elements that corresponded ## to ids **before** sorting xx <- qnorm(pp)[names(xs)[ids]] yy <- sort(x)[names(xs)][ids] ## quantile(values, pp)[ids] ltext(xx, yy, idLabels[ids], cex = dots$cex, adj = dots$adj) } } qqmath(values, xlab = "Standard normal quantiles", ylab = "Standardized residuals", prepanel = prepanel.qqmathline, panel = qqpanel, ...) } ## qqmath(~residuals(gm1)|cbpp$herd) lme4/R/profile.R0000644000176200001440000015067515113136605013116 0ustar liggesusers## --> ../man/profile-methods.Rd profnames <- function(object, signames=TRUE, useSc=isLMM(object), prefix=c("sd","cor")) { ntp <- length(getME(object,"theta")) ## return c(if(signames) sprintf(".sig%02d", seq(ntp)) else tnames(object, old=FALSE, prefix=prefix), if(useSc) if (signames) ".sigma" else "sigma") } ##' @importFrom splines backSpline interpSpline periodicSpline ##' @importFrom stats profile ##' @method profile merMod ##' @export profile.merMod <- function(fitted, which=NULL, alphamax = 0.01, maxpts = 100, delta = NULL, delta.cutoff = 1/8, verbose=0, devtol=1e-9, devmatchtol=1e-5, maxmult = 10, startmethod = "prev", optimizer = NULL, control = NULL, signames = TRUE, parallel = c("no", "multicore", "snow"), ncpus = getOption("profile.ncpus", 1L), cl = NULL, prof.scale = c("sdcor","varcov"), ...) { ## FIXME: allow choice of nextstep/nextstart algorithm? ## FIXME: allow selection of individual variables to profile by name? ## FIXME: allow for failure of bounds (non-pos-definite correlation matrices) when >1 cor parameter prof.scale <- match.arg(prof.scale) parallel <- match.arg(parallel) do_parallel <- have_mc <- have_snow <- NULL # "-Wall" are set here: eval(initialize.parallel)# (parallel, ncpus) if (is.null(optimizer)) optimizer <- fitted@optinfo$optimizer ## hack: doesn't work to set bobyqa parameters to *ending* values stored ## in @optinfo$control ignore.pars <- c("xst", "xt") control.internal <- fitted@optinfo$control if (length(ign <- which(names(control.internal) %in% ignore.pars)) > 0) control.internal <- control.internal[-ign] if (!is.null(control)) { i <- names(control) control.internal[[i]] <- control[[i]] } control <- control.internal useSc <- isLMM(fitted) || isNLMM(fitted) prof.prefix <- switch(prof.scale, "sdcor" = { transfuns <- list(from.chol= Cv_to_Sv, to.chol = Sv_to_Cv, to.sd = identity) c("sd", "cor") }, "varcov" = { transfuns <- list(from.chol= Cv_to_Vv, to.chol = Vv_to_Cv, to.sd = sqrt) c("var", "cov") }, stop("internal error, prof.scale=", prof.scale)) dd <- devfun2(fitted, useSc=useSc, signames=signames, transfuns=transfuns, prefix=prof.prefix, ...) ## FIXME: figure out to what do here ... if (isGLMM(fitted) && fitted@devcomp$dims[["useSc"]]) stop("can't (yet) profile GLMMs with non-fixed scale parameters") stopifnot(devtol >= 0) base <- attr(dd, "basedev") ## protect against accidental tampering by later devfun calls thopt <- forceCopy(attr(dd, "thopt")) stderr <- attr(dd, "stderr") pp <- environment(dd)$pp X.orig <- pp$X n <- environment(dd)$n p <- length(pp$beta0) opt <- attr(dd, "optimum") nptot <- length(opt) nvp <- nptot - p # number of variance-covariance pars wi.vp <- seq_len(nvp) if(nvp > 0) fe.orig <- opt[- wi.vp] which <- get.which(which, nvp, nptot, names(opt), verbose) res <- c(.zeta = 0, opt) res <- matrix(res, nrow = maxpts, ncol = length(res), dimnames = list(NULL, names(res)), byrow = TRUE) ## FIXME: why is cutoff based on nptot ## (i.e. boundary of simultaneous LRT conf region for nptot values) ## when we are computing (at most) 2-parameter profiles here? cutoff <- sqrt(qchisq(1 - alphamax, nptot)) if (is.null(delta)) delta <- cutoff*delta.cutoff ## helper functions ## nextpar calculates the next value of the parameter being ## profiled based on the desired step in the profile zeta ## (absstep) and the values of zeta and column cc for rows ## r-1 and r. The parameter may not be below lower (or above upper) nextpar <- function(mat, cc, r, absstep, lower = -Inf, upper = Inf, minstep=1e-6) { rows <- r - (1:0) # previous two row numbers pvals <- mat[rows, cc] zeta <- mat[rows, ".zeta"] num <- diff(pvals) if (is.na(denom <- diff(zeta)) || denom==0) { warning("Last two rows have identical or NA .zeta values: using minstep") step <- minstep } else { step <- absstep*num/denom if (step<0) { warning("unexpected decrease in profile: using minstep") step <- minstep } else { if (r>1) { if (abs(step) > (maxstep <- abs(maxmult*num))) { maxstep <- sign(step)*maxstep if (verbose) cat(sprintf("capped step at %1.2f (multiplier=%1.2f > %1.2f)\n", maxstep,abs(step/num),maxmult)) step <- maxstep } } } } min(upper, max(lower, pvals[2] + sign(num) * step)) } nextstart <- function(mat, pind, r, method) { ## FIXME: indexing may need to be checked (e.g. for fixed-effect parameters) switch(method, global= opt[seqpar1][-pind], ## address opt, no zeta column prev = mat[r,1+seqpar1][-pind], extrap = stop("not yet implemented"),## do something with mat[r-(1:0),1+seqnvp])[-pind] stop("invalid nextstart method")) } ## mkpar generates the parameter vector of theta and ## sigma from the values being profiled in position w mkpar <- function(np, w, pw, pmw) { par <- numeric(np) par[w] <- pw par[-w] <- pmw par } ## fillmat fills the third and subsequent rows of the matrix ## using nextpar and zeta ## FIXME: add code to evaluate more rows near the minimum if that ## constraint was active. fillmat <- function(mat, lowcut, upcut, zetafun, cc) { nr <- nrow(mat) i <- 2L while (i < nr && mat[i, cc] > lowcut && mat[i,cc] < upcut && (is.na(curzeta <- abs(mat[i, ".zeta"])) || curzeta <= cutoff)) { np <- nextpar(mat, cc, i, delta, lowcut, upcut) ns <- nextstart(mat, pind = cc-1, r=i, method=startmethod) mat[i + 1L, ] <- zetafun(np,ns) if (verbose>0) { cat(i,cc,mat[i+1L,],"\n") } i <- i + 1L } if (mat[i-1,cc]==lowcut) { ## fill in more values near the minimum } if (mat[i-1,cc]==upcut) { ## fill in more values near the maximum } mat } ## bounds on Cholesky (== fitted@lower): [0,Inf) for diag, (-Inf,Inf) for off-diag ## bounds on sd-corr: [0,Inf) for diag, (-1.0,1.0) for off-diag ## bounds on var-corr: [0,Inf) for diag, (-Inf,Inf) for off-diag if (prof.scale=="sdcor") { lower <- pmax(fitted@lower, -1.) upper <- ifelse(fitted@lower==0, Inf, 1.0) } else { lower <- fitted@lower upper <- rep(Inf,length.out=length(fitted@lower)) } if (useSc) { # bounds for sigma lower <- c(lower,0) upper <- c(upper,Inf) } ## bounds on fixed parameters (TODO: allow user-specified bounds, e.g. for NLMMs) lower <- c(lower,rep.int(-Inf, p)) upper <- c(upper, rep.int(Inf, p)) npar1 <- if (isLMM(fitted)) nvp else nptot ## check that devfun2() computation for the base parameters is (approx.) the ## same as the original devfun() computation basecheck <- all.equal(unname(dd(opt[seq(npar1)])), base, tolerance=devmatchtol) if(!isTRUE(basecheck)) { basediff <- abs(dd(opt[seq(npar1)])/base - 1) stop(sprintf(paste0("Profiling over both the residual variance and\n", "fixed effects is not numerically consistent with\n", "profiling over the fixed effects only (relative difference: %1.4g);\n", "consider adjusting devmatchtol"), basediff)) } ## sequence of variance parameters to profile seqnvp <- intersect(seq_len(npar1), which) ## sequence of 'all' parameters seqpar1 <- seq_len(npar1) lowvp <- lower[seqpar1] upvp <- upper[seqpar1] form <- .zeta ~ foo # pattern for interpSpline formula ## as in bootMer.R, define FUN as a ## closure containing the referenced variables ## in its scope to avoid explicit clusterExport statement ## in the PSOCKcluster case FUN <- local({ function(w) { if (verbose) cat(if(isLMM(fitted)) "var-cov " else "", "parameter ",w,":\n",sep="") wp1 <- w + 1L pw <- opt[w] lowcut <- lower[w] upcut <- upper[w] zeta <- function(xx,start) { ores <- tryCatch(optwrap(optimizer, par=start, fn=function(x) dd(mkpar(npar1, w, xx, x)), lower = lowvp[-w], upper = upvp [-w], control = control), error=function(e) NULL) if (is.null(ores)) { devdiff <- NA pars <- NA } else { devdiff <- ores$fval - base pars <- ores$par } if (is.na(devdiff)) { warning("NAs detected in profiling") } else { if(verbose && devdiff < 0) cat("old deviance ",base,",\n", "new deviance ",ores$fval,",\n", "new params ", paste(mkpar(npar1,w,xx,ores$par), collapse=","),"\n") if (devdiff < (-devtol)) stop("profiling detected new, lower deviance ", sprintf("(deviance diff = %1.3g, tolerance = %1.3g)", abs(devdiff), devtol)) if(devdiff < 0) warning(gettextf("slightly lower deviances (diff=%g) detected", devdiff), domain=NA) } devdiff <- max(0,devdiff) zz <- sign(xx - pw) * sqrt(devdiff) r <- c(zz, mkpar(npar1, w, xx, pars)) if (isLMM(fitted)) c(r, pp$beta(1)) else r }## {zeta} ## intermediate storage for pos. and neg. increments pres <- nres <- res ## assign one row, determined by inc. sign, from a small shift ## FIXME:: do something if pw==0 ??? shiftpar <- if (pw==0) 1e-3 else pw*1.01 ## Since both the pos- and neg-increment matrices are already ## filled with the opt. par. results, this sets the first ## two rows of the positive-increment matrix ## to (opt. par, shiftpar) and the first two rows of ## the negative-increment matrix to (shiftpar, opt. par), ## which sets up two points going in the right direction ## for each matrix (since the profiling algorithm uses increments ## between rows to choose the next parameter increment) nres[1, ] <- pres[2, ] <- zeta(shiftpar, start=opt[seqpar1][-w]) ## fill in the rest of the arrays and collapse them upperf <- fillmat(pres, lowcut, upcut, zeta, wp1) lowerf <- if (pw > lowcut) fillmat(nres, lowcut, upcut, zeta, wp1) else ## don't bother to fill in 'nres' matrix nres ## this will throw away the extra 'opt. par' and 'shiftpar' ## rows introduced above: bres <- as.data.frame(unique(rbind2(upperf,lowerf))) pname <- names(opt)[w] bres$.par <- pname bres <- bres[order(bres[, wp1]), ] ## FIXME: test for bad things here?? form[[3]] <- as.name(pname) forspl <- NULL # (in case of error) ## bakspl bakspl <- tryCatch(backSpline( forspl <- interpSpline(form, bres, na.action=na.omit)), error=function(e)e) if (inherits(bakspl, "error")) warning("non-monotonic profile for ",pname) ## return: namedList(bres,bakspl,forspl) # namedList() -> lmerControl.R }}) ## FUN() ## copied from bootMer: DRY! L <- if (do_parallel) { if (have_mc) { parallel::mclapply(seqnvp, FUN, mc.cores = ncpus) } else if (have_snow) { if (is.null(cl)) { cl <- parallel::makePSOCKcluster(rep("localhost", ncpus)) ## explicit export of the lme4 namespace since most FUNs will probably ## use some of them parallel::clusterExport(cl, varlist=getNamespaceExports("lme4")) if(RNGkind()[1L] == "L'Ecuyer-CMRG") parallel::clusterSetRNGStream(cl) pres <- parallel::parLapply(cl, seqnvp, FUN) parallel::stopCluster(cl) pres } else parallel::parLapply(cl, seqnvp, FUN) } } else lapply(seqnvp, FUN) nn <- names(opt[seqnvp]) ans <- setNames(lapply(L, `[[`, "bres"), nn) bakspl <- setNames(lapply(L, `[[`,"bakspl"), nn) forspl <- setNames(lapply(L, `[[`,"forspl"), nn) ## profile fixed effects separately (for LMMs) if (isLMM(fitted)) { offset.orig <- fitted@resp$offset fp <- seq_len(p) fp <- fp[(fp+nvp) %in% which] ## FIXME: parallelize this too ... for (j in fp) { if (verbose) cat("fixed-effect parameter ",j,":\n",sep="") pres <- # intermediate results for pos. incr. nres <- res # and negative increments est <- opt[nvp + j] std <- stderr[j] Xw <- X.orig[, j, drop=TRUE] Xdrop <- .modelMatrixDrop(X.orig, j) pp1 <- new(class(pp), X = Xdrop, Zt = pp$Zt, Lambdat = pp$Lambdat, Lind = pp$Lind, theta = pp$theta, n = nrow(Xdrop)) ### FIXME Change this to use the deep copy and setWeights, setOffset, etc. rr <- new(Class=class(fitted@resp), y=fitted@resp$y) rr$setWeights(fitted@resp$weights) fe.zeta <- function(fw, start) { ## (start parameter ignored) rr$setOffset(Xw * fw + offset.orig) rho <- list2env(list(pp=pp1, resp=rr), parent = parent.frame()) ores <- optwrap(optimizer, par = thopt, fn = mkdevfun(rho, 0L), lower = fitted@lower) ## this optimization is done on the ORIGINAL ## theta scale (i.e. not the sigma/corr scale) ## upper=Inf for all cases ## lower = pmax(fitted@lower, -1.0), ## upper = 1/(fitted@lower != 0))## = ifelse(fitted@lower==0, Inf, 1.0) fv <- ores$fval sig <- sqrt((rr$wrss() + pp1$sqrL(1))/n) c(sign(fw - est) * sqrt(fv - base), Cv_to_Sv(ores$par, lengths(fitted@cnms), s=sig), ## ores$par * sig, sig, mkpar(p, j, fw, pp1$beta(1))) } nres[1, ] <- pres[2, ] <- fe.zeta(est + delta * std) poff <- nvp + 1L + j ## Workaround R bug [rbind2() is S4 generic; cannot catch warnings in its arg] ## see lme4 GH issue #304 upperf <- fillmat(pres, -Inf, Inf, fe.zeta, poff) lowerf <- fillmat(nres, -Inf, Inf, fe.zeta, poff) bres <- as.data.frame(unique(rbind2(upperf, lowerf))) bres$.par <- n.j <- names(fe.orig)[j] ans[[n.j]] <- bres[order(bres[, poff]), ] form[[3]] <- as.name(n.j) bakspl[[n.j]] <- tryCatch(backSpline(forspl[[n.j]] <- interpSpline(form, bres)), error=function(e)e) if (inherits(bakspl[[n.j]], "error")) warning("non-monotonic profile for ", n.j) } ## for(j in 1..p) } ## if isLMM ans <- do.call(rbind, ans) row.names(ans) <- NULL ## TODO: rbind(*, make.row.names=FALSE) ans$.par <- factor(ans$.par) structure(ans, forward = forspl, backward = bakspl, lower = lower[seqnvp], upper = upper[seqnvp], class = c("thpr", "data.frame"))# 'thpr': see ../man/profile-methods.Rd } ## profile.merMod ##' Transform 'which' \in {parnames | integer | "beta_" | "theta_"} ##' into integer indices ##' @param which numeric or character vector ##' @param nvp number of variance-covariance parameters ##' @param nptot total number of parameters ##' @param parnames vector of parameter names ##' @param verbose print messages? ##' @examples ##' fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) ##' tn <- names(getME(fm1,"theta")) ##' nn <- c(tn,names(fixef(fm1))) ##' get.which("theta_",length(tn),length(nn),nn, verbose=TRUE) ##' get.which <- function(which, nvp, nptot, parnames, verbose=FALSE) { if (is.null(which)) seq_len(nptot) else if (is.character(which)) { wi <- integer(); wh <- which if(any(j <- wh == "theta_")) { wi <- seq_len(nvp); wh <- wh[!j] } if(any(j <- wh == "beta_") && nptot > nvp) { wi <- c(wi, seq(nvp+1, nptot)); wh <- wh[!j] } if(any(j <- parnames %in% wh)) { ## which containing param.names wi <- sort(unique(c(wi, seq_len(nptot)[j]))) } if(verbose) message(gettextf("From original which = %s: new which <- %s", deparse(which, nlines=1), deparse(wi, nlines=1)), domain=NA) if(length(wi) == 0) warning(gettextf("Nothing selected by 'which=%s'", deparse(which)), domain=NA) wi } else # stopifnot( .. numeric ..) which } ## This is a hack. The preferred approach is to write a ## subset method for the ddenseModelMatrix and dsparseModelMatrix ## classes .modelMatrixDrop <- function(mm, w) { if (isS4(mm)) { nX <- slotNames(X <- mm[, -w, drop = FALSE]) do.call(new, c(list(Class = class(mm), assign = attr(mm,"assign")[-w], contrasts = NULL ## FIXME: where did the contrasts information go?? ## mm@contrasts ), lapply(structure(nX, .Names=nX), function(nm) slot(X, nm)))) } else { structure(mm[, -w, drop=FALSE], assign = attr(mm, "assign")[-w]) } } ## The deviance is profiled with respect to the fixed-effects ## parameters but not with respect to sigma. The other parameters ## are on the standard deviation scale, not the theta scale. ## ## @title Return a function for evaluation of the deviance. ## @param fm a fitted model of class merMod ## @param useSc (logical) whether a scale parameter is used ## @param \dots arguments passed to profnames (\code{signames=TRUE} ## to use old-style .sigxx names, FALSE uses (sd_cor|xx); ## also \code{prefix=c("sd","cor")}) ## @return a function for evaluating the deviance in the extended ## parameterization. This is profiled with respect to the ## variance-covariance parameters (fixed-effects done separately). devfun2 <- function(fm, useSc = if(isLMM(fm)) TRUE else NA, transfuns = list(from.chol = Cv_to_Sv, to.chol = Sv_to_Cv, to.sd = identity), ...) { ## FIXME: have to distinguish between ## 'useSc' (GLMM: report profiled scale parameter) and ## 'useSc' (NLMM/LMM: scale theta by sigma) ## hasSc := GLMMuseSc <- fm@devcomp$dims["useSc"] stopifnot(is(fm, "merMod")) fm <- refitML(fm) basedev <- -2*c(logLik(fm)) ## no longer deviance() vlist <- lengths(fm@cnms) ## "has scale" := isLMM or GLMM with scale parameter hasSc <- as.logical(fm@devcomp$dims[["useSc"]]) stdErr <- unname(coef(summary(fm))[,2]) pp <- fm@pp$copy() if (useSc) { sig <- sigma(fm) ## only if hasSc is TRUE? ## opt <- c(pp$theta*sig, sig) opt <- transfuns$from.chol(pp$theta, n=vlist, s=sig) } else { opt <- transfuns$from.chol(pp$theta, n=vlist) } names(opt) <- profnames(fm, useSc=useSc, ...) opt <- c(opt, fixef(fm)) resp <- fm@resp$copy() np <- length(pp$theta) nf <- length(fixef(fm)) if(hasSc) np <- np + 1L # was if(!isGLMM(fm)) np <- np + 1L n <- nrow(pp$V) # use V, not X so it works with nlmer if (isLMM(fm)) { # ==> hasSc ans <- function(pars) { stopifnot(is.numeric(pars), length(pars) == np) ## Assumption: we can translate the *last* parameter back ## to sigma (SD) scale ... sigma <- transfuns$to.sd(pars[np]) ## .Call(lmer_Deviance, pp$ptr(), resp$ptr(), pars[-np]/sigma) ## convert from sdcor vector back to 'unscaled theta' thpars <- transfuns$to.chol(pars, n=vlist, s=sigma) .Call(lmer_Deviance, pp$ptr(), resp$ptr(), thpars) sigsq <- sigma^2 pp$ldL2() - ldW + (resp$wrss() + pp$sqrL(1))/sigsq + n * log(2 * pi * sigsq) } ldW <- sum(log(environment(ans)$resp$weights)) assign("ldW", ldW, envir = environment(ans)) } else { # GLMM *and* NLMMs d0 <- getME(fm, "devfun") ## from glmer: ## rho <- new.env(parent=parent.env(environment())) ## rho$pp <- do.call(merPredD$new, c(reTrms[c("Zt","theta","Lambdat","Lind")], n=nrow(X), list(X=X))) ## rho$resp <- mkRespMod(fr, if(REML) p else 0L) ans <- function(pars) { stopifnot(is.numeric(pars), length(pars) == np+nf) thpars <- if(!useSc) transfuns$to.chol(pars[seq(np)], n=vlist) else transfuns$to.chol(pars[seq(np)], n=vlist, s=pars[np]) fixpars <- pars[-seq(np)] d0(c(thpars,fixpars)) } } attr(ans, "optimum") <- opt # w/ names() attr(ans, "basedev") <- basedev attr(ans, "thopt") <- pp$theta attr(ans, "stderr") <- stdErr class(ans) <- "devfun" ans } ## extract only the y component from a prediction predy <- function(sp, vv) { if(inherits(sp,"error")) rep(NA_real_, length(vv)) else predict(sp, vv)$y } stripExpr <- function(ll, nms) { stopifnot(is.list(ll), is.character(nms)) fLevs <- as.expression(nms) # variable names; now replacing log(sigma[.]) etc: fLevs[nms == ".sigma"] <- expression(sigma) fLevs[nms == ".lsigma"] <- expression(log(sigma)) fLevs[nms == ".sigmasq"] <- expression(sigma^2) sigNms <- grep("^\\.sig[0-9]+", nms) lsigNms <- grep("^\\.lsig[0-9]+", nms) sig2Nms <- grep("^\\.sigsq[0-9]+", nms) ## the in ".sig0" and then in ".lsig0": sigsub <- as.integer(substring(nms[ sigNms], 5)) lsigsub <- as.integer(substring(nms[lsigNms], 6)) sig2sub <- as.integer(substring(nms[sig2Nms], 7)) fLevs[ sigNms] <- lapply( sigsub, function(i) bquote( sigma[.(i)])) fLevs[lsigNms] <- lapply(lsigsub, function(i) bquote(log(sigma[.(i)]))) fLevs[sig2Nms] <- lapply(sig2sub, function(i) bquote( {sigma[.(i)]}^2)) ## result of using { .. }^2 is easier to understand == == levsExpr <- substitute(strip.custom(factor.levels=foo), list(foo=fLevs)) snames <- c("strip", "strip.left") if(!any(.in <- snames %in% names(ll))) { ll$strip <- levsExpr } else { for(nm in snames[.in]) if(is.logical(v <- ll[[nm]]) && v) ll[[nm]] <- levsExpr } ll } panel.thpr <- function(x, y, spl, absVal, ...) { panel.grid(h = -1, v = -1) myspl <- spl[[panel.number()]] lsegments(x, y, x, 0, ...) if (absVal) { y[y == 0] <- NA lsegments(x, y, rev(x), y) } else { panel.abline(h = 0, ...) } if (!is(myspl,"spline")) { ## 'difficult' data if (absVal) myspl$y <- abs(myspl$y) panel.lines (myspl$x, myspl$y) panel.points(myspl$x, myspl$y, pch="+") warning(gettextf("bad profile for variable %d: using linear interpolation", panel.number()), domain=NA) } else { lims <- current.panel.limits()$xlim krange <- range(myspl$knots) pr <- predict(myspl, seq(max(lims[1], krange[1]), min(lims[2], krange[2]), len = 101)) if (absVal) pr$y <- abs(pr$y) panel.lines(pr$x, pr$y) } } ## A lattice-based plot method for profile objects ##' @importFrom lattice xyplot ##' @S3method xyplot thpr xyplot.thpr <- function (x, data = NULL, levels = sqrt(qchisq(pmax.int(0, pmin.int(1, conf)), df = 1)), conf = c(50, 80, 90, 95, 99)/100, absVal = FALSE, scales = NULL, which = 1:nptot, ...) { if(any(!is.finite(conf) | conf <= 0 | conf >= 1)) stop("values of 'conf' must be strictly between 0 and 1") stopifnot(1 <= (nptot <- length(nms <- levels(x[[".par"]])))) ## FIXME: is this sufficiently reliable? ## (include "sigma" in 'theta' parameters) nvp <- length(grep("^(\\.sig[0-9]+|.sigma|sd_|cor_)", nms)) which <- get.which(which, nvp, nptot, nms) levels <- sort(levels[is.finite(levels) & levels > 0]) spl <- attr(x, "forward") [which] bspl <- attr(x, "backward")[which] ## for parameters for which spline couldn't be computed, ## replace the 'spl' element with the raw profile data if(any(badSpl <- vapply(spl, is.null, NA))) { spl[badSpl] <- lapply(which(badSpl), function(i) { n <- names(badSpl)[i] r <- x[x[[".par"]] == n, ] data.frame(y = r[[".zeta"]], x = r[[n]]) }) bspl[badSpl] <- lapply(spl[badSpl], function(d) data.frame(x=d$y,y=d$x)) ## FIXME: more efficient, not yet ok ? ## ibad <- which(badSpl) ## spl[ibad] <- lapply(names(ibad), function(n) { ## r <- x[x[[".par"]]==n,] ## data.frame(y = r[[".zeta"]], x = r[[n]]) ## }) ## bspl[ibad] <- lapply(spl[ibad], function(d) data.frame(x=d$y,y=d$x)) } zeta <- c(-rev(levels), 0, levels) mypred <- function(bs, zeta) { ## use linear approximation if backspline doesn't work if (inherits(bs,"spline")) predy(bs, zeta) else if(is.numeric(x <- bs$x) && is.numeric(y <- bs$y) && length(x) == length(y)) approx(x, y, xout = zeta)$y else rep_len(NA, length(zeta)) } fr <- data.frame(zeta = rep.int(zeta, length(spl)), pval = unlist(lapply(bspl, mypred, zeta)), pnm = gl(length(spl), length(zeta), labels = names(spl))) if (length(ind <- which(is.na(fr$pval)))) { fr[ind, "zeta"] <- 0 for (i in ind) ### FIXME: Should check which bound has been violated, although it ### will almost always be the minimum. if (inherits(curspl <- spl[[fr[i, "pnm"] ]], "spline")) { fr[i, "pval"] <- min(curspl$knots) } } ylab <- if (absVal) { fr$zeta <- abs(fr$zeta) expression("|" * zeta * "|") } else expression(zeta) intscales <- list(x = list(relation = 'free')) ## FIXME: is there something less clunky we can do here ## that allows for all possible user inputs ## (may want to (1) override x$relation (2) add elements to $x ## (3) add elements to scales) if (!is.null(scales)) { if (!is.null(scales[["x"]])) { if (!is.null(scales[["x"]]$relation)) { intscales[["x"]]$relation <- scales[["x"]]$relation scales[["x"]]$relation <- NULL } intscales[["x"]] <- c(intscales[["x"]],scales[["x"]]) scales[["x"]] <- NULL } intscales <- c(intscales,scales) } ll <- c(list(...), list(x = zeta ~ pval | pnm, data=fr, scales = intscales, ylab = ylab, xlab = NULL, panel=panel.thpr, spl = spl, absVal = absVal)) do.call(xyplot, stripExpr(ll, names(spl))) } ## copy of stats:::format.perc (not exported, and ":::" being forbidden nowadays): format.perc <- function (x, digits, ...) { paste(format(100 * x, trim = TRUE, scientific = FALSE, digits = digits), "%") } ##' confint() method for our profile() results 'thpr' ##' @importFrom stats confint confint.thpr <- function(object, parm, level = 0.95, zeta, ## tolerance for non-monotonic profiles ## (raw values, not splines) non.mono.tol=1e-2, ...) { bak <- attr(object, "backward") ## fallback strategy for old profiles that don't have a lower/upper ## attribute saved ... if (is.null(lower <- attr(object,"lower"))) lower <- rep(NA,length(parm)) if (is.null(upper <- attr(object,"upper"))) upper <- rep(NA,length(parm)) ## FIXME: work a little harder to add -Inf/Inf for fixed effect ## parameters? (Should only matter for really messed-up profiles) bnms <- names(bak) parm <- if (missing(parm)) bnms else if(is.numeric(parm)) # e.g., when called from confint.merMod() bnms[parm] else if (length(parm <- as.character(parm)) == 1) { if (parm == "theta_") grep("^(sd_|cor_|.sig|sigma$)", bnms, value=TRUE) else if (parm == "beta_") grep("^(sd_|cor_|.sig|sigma$)", bnms, value=TRUE, invert=TRUE) else if(parm %in% bnms) # just that one parm ## else NULL : will return 0-row matrix } else intersect(parm, bnms) cn <- if (missing(zeta)) { a <- (1 - level)/2 a <- c(a, 1 - a) zeta <- qnorm(a) format.perc(a, 3) } ## else NULL ci <- matrix(NA_real_, nrow=length(parm), ncol=2L, dimnames = list(parm,cn)) for (i in seq_along(parm)) { ## would like to build this machinery into predy, but ## predy is used in many places and it's much harder to ## tell in general whether an NA indicates a lower or an ## upper bound ... badprof <- FALSE p <- rep(NA,2) if (!inherits(b <- bak[[parm[i]]], "error")) { p <- predy(b, zeta) } else { obj1 <- object[object$.par==parm[[i]],c(parm[[i]],".zeta")] if (all(is.na(obj1[,2]))) { badprof <- TRUE warning("bad profile for ",parm[i]) } else if (min(diff(obj1[,2])<(-non.mono.tol),na.rm=TRUE)) { badprof <- TRUE warning("non-monotonic profile for ",parm[i]) } else { warning("bad spline fit for ",parm[i],": falling back to linear interpolation") p <- approxfun(obj1[,2],obj1[,1])(zeta) } } if (!badprof) { if (is.na(p[1])) p[1] <- lower[i] if (is.na(p[2])) p[2] <- upper[i] } ci[i,] <- p } ci } ## FIXME: make bootMer more robust; make profiling more robust; ## more warnings; documentation ... ##' Compute confidence intervals on the parameters of an lme4 fit ##' @param object a fitted [ng]lmer model ##' @param parm parameters (specified by integer position) ##' @param level confidence level ##' @param method for computing confidence intervals ##' @param zeta likelihood cutoff ##' (if not specified, computed from \code{level}: "profile" only) ##' @param nsim number of simulations for parametric bootstrap intervals ##' @param boot.type bootstrap confidence interval type ##' @param quiet (logical) suppress messages about computationally intensive profiling? ##' @param signames (logical) use old-style names for \code{method="profile"}? (See \code{signames} argument to \code{\link{profile}} ##' @param oldNames (logical) deprecated; has the same purpose as the \code{signames} argument. ##' @param \dots additional parameters to be passed to \code{\link{profile.merMod}} or \code{\link{bootMer}} ##' @return a numeric table of confidence intervals confint.merMod <- function(object, parm, level = 0.95, method = c("profile","Wald","boot"), zeta, nsim=500, boot.type = c("perc","basic","norm"), FUN = NULL, quiet=FALSE, oldNames, signames = TRUE, ...) { method <- match.arg(method) boot.type <- match.arg(boot.type) if (!missing(oldNames)) { warning("'oldNames' is deprecated. Please use 'signames' instead.", call. = FALSE) signames <- oldNames } ## 'parm' corresponds to 'which' in other contexts if (method=="boot" && !is.null(FUN)) { ## custom boot function, don't expand parameter names } else { ## "use scale" = GLMM with scale parameter *or* LMM .. useSc <- as.logical(object@devcomp$dims[["useSc"]]) vn <- profnames(object, signames, useSc=useSc) an <- c(vn,names(fixef(object))) parm <- if(missing(parm)) seq_along(an) else get.which(parm, nvp=length(vn), nptot=length(an), parnames=an) if (!quiet && method %in% c("profile","boot")) { mtype <- switch(method, profile="profile", boot="bootstrap") message("Computing ",mtype," confidence intervals ...") flush.console() } } switch(method, "profile" = { pp <- profile(object, which=parm, signames=signames, ...) ## confint.thpr() with missing(parm) using all names: confint(pp, level=level, zeta=zeta) }, "Wald" = { a <- (1 - level)/2 a <- c(a, 1 - a) ci.vcov <- array(NA,dim = c(length(vn), 2L), dimnames = list(vn, format.perc(a,3))) ## copied with small changes from confint.default cf <- fixef(object) ## coef() -> fixef() pnames <- names(cf) ## N.B. can't use sqrt(...)[parm] (diag() loses names) ses <- sqrt(diag(vcov(object))) ci.fixed <- array(cf + ses %o% qnorm(a), dim = c(length(pnames), 2L), dimnames = list(pnames, format.perc(a, 3))) vnames <- tnames(object) ci.all <- rbind(ci.vcov,ci.fixed) ci.all[parm,,drop=FALSE] }, "boot" = { bootFun <- function(x) { th <- x@theta nvec <- lengths(x@cnms) scaleTh <- (isLMM(x) || isNLMM(x)) ## FIXME: still ugly. Best cleanup via Cv_to_Sv ... ss <- if (scaleTh) { ## scale variances by sigma and include it Cv_to_Sv(th, n=nvec, s=sigma(x)) } else if (useSc) { ## don't scale variances but do include sigma c(Cv_to_Sv(th, n=nvec), sigma(x)) } else { ## no scaling, no sigma Cv_to_Sv(th, n=nvec) } c(setNames(ss, vn), fixef(x)) } if (is.null(FUN)) FUN <- bootFun bb <- bootMer(object, FUN=FUN, nsim=nsim,...) if (all(is.na(bb$t))) stop("*all* bootstrap runs failed!") print.bootWarnings(bb, verbose=FALSE) citab <- confint(bb,level=level,type=boot.type) if (missing(parm)) { ## only happens if we have custom boot method if (is.null(parm <- rownames(citab))) { parm <- seq(nrow(citab)) } } citab[parm, , drop=FALSE] }, ## should never get here ... stop("unknown confidence interval method")) } ##' Convert x-cosine and y-cosine to average and difference. ##' ##' Convert the x-cosine and the y-cosine to an average and difference ##' ensuring that the difference is positive by flipping signs if ##' necessary ##' @param xc x-cosine ##' @param yc y-cosine ad <- function(xc, yc) { a <- (xc + yc)/2 d <- (xc - yc) cbind(sign(d)* a, abs(d)) } ##' convert d versus a (as an xyVector) and level to a matrix of taui and tauj ##' @param xy an xyVector ##' @param lev the level of the contour tauij <- function(xy, lev) lev * cos(xy$x + outer(xy$y/2, c(-1, 1))) ##' @title safe arc-cosine ##' @param x numeric vector argument ##' @return acos(x) being careful of boundary conditions sacos <- function(x) acos(pmax.int(-0.999, pmin.int(0.999, x))) ##' Generate a contour ##' ##' @title Generate a contour ##' @param sij the arc-cosines of i on j ##' @param sji the arc-cosines of j on i ##' @param levels numeric vector of levels at which to interpolate ##' @param nseg number of segments in the interpolated contour ##' @return a list with components ##' \item{tki}{the tau-scale predictions of i on j at the contour levels} ##' \item{tkj}{the tau-scale predictions of j on i at the contour levels} ##' \item{pts}{an array of dimension (length(levels), nseg, 2) containing the points on the contours} cont <- function(sij, sji, levels, nseg = 101) { ada <- array(0, c(length(levels), 2L, 4L)) ada[, , 1] <- ad(0, sacos(predy(sij, levels)/levels)) ada[, , 2] <- ad( sacos(predy(sji, levels)/levels), 0) ada[, , 3] <- ad(pi, sacos(predy(sij, -levels)/levels)) ada[, , 4] <- ad(sacos(predy(sji, -levels)/levels), pi) pts <- array(0, c(length(levels), nseg + 1, 2)) for (i in seq_along(levels)) pts[i, ,] <- tauij(predict(periodicSpline(ada[i, 1, ], ada[i, 2, ]), nseg = nseg), levels[i]) levs <- c(-rev(levels), 0, levels) list(tki = predict(sij, levs), tkj = predict(sji, levs), pts = pts) } ## copied from lattice:::chooseFace chooseFace <- function (fontface = NULL, font = 1) { if (is.null(fontface)) font else fontface } ##' Draws profile pairs plots. Contours are for the marginal ##' two-dimensional regions (i.e. using df = 2). ##' ##' @title Profile pairs plot ##' @param x the result of \code{\link{profile}} (or very similar structure) ##' @param data unused - only for compatibility with generic ##' @param levels the contour levels to be shown; usually derived from \code{conf} ##' @param conf numeric vector of confidence levels to be shown as contours ##' @param ... further arguments passed to \code{\link{splom}} ##' @importFrom grid gpar viewport ##' @importFrom lattice splom ##' @method splom thpr ##' @export splom.thpr <- function (x, data, levels = sqrt(qchisq(pmax.int(0, pmin.int(1, conf)), 2)), conf = c(50, 80, 90, 95, 99)/100, which = 1:nptot, draw.lower = TRUE, draw.upper = TRUE, ...) { stopifnot(1 <= (nptot <- length(nms <- names(attr(x, "forward"))))) singfit <- FALSE for (i in grep("^(\\.sig[0-9]+|sd_)", names(x))) singfit <- singfit || any(x[,".zeta"] == 0 & x[,i] == 0) if (singfit) warning("splom is unreliable for singular fits") nvp <- length(grep("^(\\.sig[0-9]+|.sigma|sd_|cor_)", nms)) which <- get.which(which, nvp, nptot, nms) if (length(which) == 1) stop("can't draw a scatterplot matrix for a single variable") mlev <- max(levels) spl <- attr(x, "forward")[which] frange <- sapply(spl, function(x) range(x$knots)) bsp <- attr(x, "backward")[which] x <- x[x[[".par"]] %in% nms[which],c(".zeta",nms[which],".par")] ## brange <- sapply(bsp, function(x) range(x$knots)) pfr <- do.call(cbind, lapply(bsp, predy, c(-mlev, mlev))) pfr[1, ] <- pmax.int(pfr[1, ], frange[1, ], na.rm = TRUE) pfr[2, ] <- pmin.int(pfr[2, ], frange[2, ], na.rm = TRUE) nms <- names(spl) ## Create data frame fr of par. vals in zeta coordinates fr <- x[, -1] for (nm in nms) fr[[nm]] <- predy(spl[[nm]], na.omit(fr[[nm]])) fr1 <- fr[1, nms] ## create a list of lists with the names of the parameters traces <- lapply(fr1, function(el) lapply(fr1, function(el1) list())) .par <- NULL ## => no R CMD check warning for (j in seq_along(nms)[-1]) { frj <- subset(fr, .par == nms[j]) for (i in seq_len(j - 1L)) { fri <- subset(fr, .par == nms[i]) sij <- interpSpline(fri[ , i], fri[ , j]) sji <- interpSpline(frj[ , j], frj[ , i]) ll <- cont(sij, sji, levels) traces[[j]][[i]] <- list(sij = sij, sji = sji, ll = ll) } } if(draw.lower) ## panel function for lower triangle lp <- function(x, y, groups, subscripts, i, j, ...) { tr <- traces[[j]][[i]] grid::pushViewport(viewport(xscale = c(-1.07, 1.07) * mlev, yscale = c(-1.07, 1.07) * mlev)) dd <- sapply(current.panel.limits(), diff)/50 psij <- predict(tr$sij) ll <- tr$ll ## now do the actual plotting panel.grid(h = -1, v = -1) llines(psij$y, psij$x, ...) llines(predict(tr$sji), ...) with(ll$tki, lsegments(y - dd[1], x, y + dd[1], x, ...)) with(ll$tkj, lsegments(x, y - dd[2], x, y + dd[2], ...)) for (k in seq_along(levels)) llines(ll$pts[k, , ], ...) grid::popViewport(1) } if(draw.upper) ## panel function for upper triangle up <- function(x, y, groups, subscripts, i, j, ...) { ## panels are transposed so reverse i and j jj <- i ii <- j tr <- traces[[jj]][[ii]] ll <- tr$ll pts <- ll$pts ## limits <- current.panel.limits() psij <- predict(tr$sij) psji <- predict(tr$sji) ## do the actual plotting panel.grid(h = -1, v = -1) llines(predy(bsp[[ii]], psij$x), predy(bsp[[jj]], psij$y), ...) llines(predy(bsp[[ii]], psji$y), predy(bsp[[jj]], psji$x), ...) for (k in seq_along(levels)) llines(predy(bsp[[ii]], pts[k, , 2]), predy(bsp[[jj]], pts[k, , 1]), ...) } dp <- function(x = NULL, # diagonal panel varname = NULL, limits, at = NULL, lab = NULL, draw = TRUE, varname.col = add.text$col, varname.cex = add.text$cex, varname.lineheight = add.text$lineheight, varname.font = add.text$font, varname.fontfamily = add.text$fontfamily, varname.fontface = add.text$fontface, axis.text.col = axis.text$col, axis.text.alpha = axis.text$alpha, axis.text.cex = axis.text$cex, axis.text.font = axis.text$font, axis.text.fontfamily = axis.text$fontfamily, axis.text.fontface = axis.text$fontface, axis.line.col = axis.line$col, axis.line.alpha = axis.line$alpha, axis.line.lty = axis.line$lty, axis.line.lwd = axis.line$lwd, i, j, ...) { n.var <- eval.parent(expression(n.var)) add.text <- trellis.par.get("add.text") axis.line <- trellis.par.get("axis.line") axis.text <- trellis.par.get("axis.text") if (!is.null(varname)) grid::grid.text(varname, gp = gpar(col = varname.col, cex = varname.cex, lineheight = varname.lineheight, fontface = chooseFace(varname.fontface, varname.font), fontfamily = varname.fontfamily)) if (draw) { at <- pretty(limits) sides <- c("left", "top") if (j == 1) sides <- "top" if (j == n.var) sides <- "left" for (side in sides) panel.axis(side = side, at = at, labels = format(at, trim = TRUE), ticks = TRUE, check.overlap = TRUE, half = side == "top" && j > 1, tck = 1, rot = 0, text.col = axis.text.col, text.alpha = axis.text.alpha, text.cex = axis.text.cex, text.font = axis.text.font, text.fontfamily = axis.text.fontfamily, text.fontface = axis.text.fontface, line.col = axis.line.col, line.alpha = axis.line.alpha, line.lty = axis.line.lty, line.lwd = axis.line.lwd) lims <- c(-1.07, 1.07) * mlev grid::pushViewport(viewport(xscale = lims, yscale = lims)) side <- if(j == 1) "right" else "bottom" which.half <- if(j == 1) "lower" else "upper" at <- pretty(lims) panel.axis(side = side, at = at, labels = format(at, trim = TRUE), ticks = TRUE, half = TRUE, which.half = which.half, tck = 1, rot = 0, text.col = axis.text.col, text.alpha = axis.text.alpha, text.cex = axis.text.cex, text.font = axis.text.font, text.fontfamily = axis.text.fontfamily, text.fontface = axis.text.fontface, line.col = axis.line.col, line.alpha = axis.line.alpha, line.lty = axis.line.lty, line.lwd = axis.line.lwd) grid::popViewport(1) } } panel.blank <- function(...) {} splom(~ pfr, lower.panel = if(draw.lower) lp else panel.blank, upper.panel = if(draw.upper) up else panel.blank, diag.panel = dp, ...) } ## return an lmer profile like x with all the .sigNN parameters ## replaced by .lsigNN. The forward and backward splines for ## these parameters are recalculated. -> ../man/profile-methods.Rd logProf <- function (x, base = exp(1), ranef=TRUE, sigIni = if(ranef) "sig" else "sigma") { stopifnot(inherits(x, "thpr")) cn <- colnames(x) sigP <- paste0("^\\.", sigIni) if (length(sigs <- grep(sigP, cn))) { repP <- sub("sig", ".lsig", sigIni) colnames(x) <- cn <- sub(sigP, repP, cn) levels(x[[".par"]]) <- sub(sigP, repP, levels(x[[".par"]])) names(attr(x, "backward")) <- names(attr(x, "forward")) <- sub(sigP, repP, names(attr(x, "forward"))) for (nm in cn[sigs]) { x[[nm]] <- log(x[[nm]], base = base) fr <- x[x[[".par"]] == nm & is.finite(x[[nm]]), TRUE, drop=FALSE] form <- eval(substitute(.zeta ~ nm, list(nm = as.name(nm)))) attr(x, "forward")[[nm]] <- isp <- interpSpline(form, fr) attr(x, "backward")[[nm]] <- backSpline(isp) } ## eliminate rows that produced non-finite logs x <- x[apply(is.finite(as.matrix(x[, sigs])), 1, all), , drop=FALSE] } x } ## the log() method must have (x, base); no other arguments log.thpr <- function (x, base = exp(1)) logProf(x, base=base) ##' Create an approximating density from a profile object -- called only from densityplot.thpr() ##' ##' @title Approximate densities from profiles ##' @param pr a profile object ##' @param npts number of points at which to evaluate the density ##' @param upper upper bound on cumulative for a cutoff ##' @return a data frame dens <- function(pr, npts=201, upper=0.999) { npts <- as.integer(npts) spl <- attr(pr, "forward") bspl <- attr(pr, "backward") stopifnot(inherits(pr, "thpr"), npts > 0, is.numeric(upper), 0.5 < upper, upper < 1, identical((vNms <- names(spl)), names(bspl))) bad_vars <- character(0) zeta <- c(-1,1) * qnorm(upper) rng <- vector(mode="list", length=length(bspl)) names(rng) <- vNms dd <- rng # empty named list to be filled for (i in seq_along(bspl)) { if (inherits(bspl[[i]], "error")) { rng[[i]] <- rep(NA,npts) bad_vars <- c(bad_vars, vNms[i]) next } rng0 <- predy(bspl[[i]], zeta) if (is.na(rng0[1])) rng0[1] <- 0 if (is.na(rng0[2])) { ## try harder to pick an upper bound for(upp in 1 - 10^seq(-4,-1, length=21)) { # <<-- TODO: should be related to 'upper' if(!is.na(rng0[2L] <- predy(bspl[[i]], qnorm(upp)))) break } if (is.na(rng0[2])) { warning("can't find an upper bound for the profile") bad_vars <- c(bad_vars, vNms[i]) next } } rng[[i]] <- seq(rng0[1], rng0[2], len=npts) } fr <- data.frame(pval = unlist(rng), pnm = gl(length(rng), npts, labels=vNms)) for (nm in vNms) { dd[[nm]] <- if (!inherits(spl[[nm]], "spline")) { rep(NA_real_, npts) } else { zz <- predy(spl[[nm]], rng[[nm]]) dnorm(zz) * predict(spl[[nm]], rng[[nm]], deriv = 1L)$y } } fr$density <- unlist(dd) if (length(bad_vars)) warning("unreliable profiles for some variables skipped: ", paste(bad_vars, collapse=", ")) fr } ##' Densityplot method for a mixed-effects model profile ## ##' @title densityplot from a mixed-effects profile ##' @param x a mixed-effects profile ##' @param data not used - for compatibility with generic ##' @param ... optional arguments to \code{\link[lattice]{densityplot}()} ##' from package \pkg{lattice}. ##' @return a density plot ##' @examples ## see example("profile.merMod") ##' @importFrom lattice densityplot ##' @method densityplot thpr ##' @export densityplot.thpr <- function(x, data, npts=201, upper=0.999, ...) { dd <- dens(x, npts=npts, upper=upper) ll <- c(list(...), list(x=density ~ pval|pnm, data=dd, type=c("l","g"), scales=list(relation="free"), xlab=NULL)) do.call(xyplot, stripExpr(ll, names(attr(x, "forward")))) } ##' Transform a mixed-effects profile to the variance scale varianceProf <- function(x, ranef=TRUE) { ## "parallel" to logProf() stopifnot(inherits(x, "thpr")) cn <- colnames(x) if(length(sigs <- grep(paste0("^\\.", if(ranef)"sig" else "sigma"), cn))) { ## s/sigma/sigmasq/ ; s/sig01/sig01sq/ etc sigP <- paste0("^(\\.sig", if(ranef) "(ma)?" else "ma", ")") repP <- "\\1sq" colnames(x) <- cn <- sub(sigP, repP, cn) levels(x[[".par"]]) <- sub(sigP, repP, levels(x[[".par"]])) names(attr(x, "backward")) <- names(attr(x, "forward")) <- sub(sigP, repP, names(attr(x, "forward"))) for (nm in cn[sigs]) { x[[nm]] <- x[[nm]]^2 ## select rows (and currently drop extra attributes) fr <- x[x[[".par"]] == nm, TRUE, drop=FALSE] form <- eval(substitute(.zeta ~ nm, list(nm = as.name(nm)))) attr(x, "forward")[[nm]] <- isp <- interpSpline(form, fr) attr(x, "backward")[[nm]] <- backSpline(isp) } } x } ## convert profile to data frame, adding a .focal parameter to simplify lattice/ggplot plotting ##' @method as.data.frame thpr ##' @param x the result of \code{\link{profile}} (or very similar structure) ##' @export ##' @rdname profile-methods as.data.frame.thpr <- function(x,...) { class(x) <- "data.frame" m <- as.matrix(x[,seq(ncol(x))-1]) ## omit .par x.p <- x[[".par"]] x[[".focal"]] <- m[cbind(seq(nrow(x)),match(x.p,names(x)))] x[[".par"]] <- factor(x.p, levels=unique(as.character(x.p))) ## restore order x } lme4/R/reformulas_imports.R0000644000176200001440000000120315103764665015404 0ustar liggesusers## transient patches for moving formula manipulation machinery to lme4 without breaking downstream packages mkWarnFun <- function(FUN) { fn <- function(...) { msg <- sprintf("the %s function has moved to the reformulas package. Please update your imports, or ask an upstream package maintainter to do so.", sQuote(FUN)) rlang::warn(msg, .frequency = "once", .frequency_id = FUN) reformulas_fun <- getExportedValue("reformulas", FUN) reformulas_fun(...) } assign(FUN, fn, envir = parent.frame()) } for (f in c("findbars","subbars", "nobars", "mkReTrms", "expandDoubleVerts", "isNested")) { mkWarnFun(f) } lme4/R/mcmcsamp.R0000644000176200001440000000654314677066752013273 0ustar liggesusers##' @name pvalues ##' @aliases mcmcsamp ##' @title Getting p-values for fitted models ##' ##' @description One of the most frequently asked questions about \code{lme4} ##' is "how do I calculate p-values for estimated parameters?" ##' Previous versions of \code{lme4} provided the \code{mcmcsamp} ##' function, which efficiently generated a Markov chain Monte Carlo sample ##' from the posterior distribution of the parameters, assuming ##' flat (scaled likelihood) priors. Due to difficulty in ##' constructing a version of \code{mcmcsamp} that was reliable ##' even in cases where the estimated random effect variances were near ##' zero (e.g. \url{https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q4/003115.html}), \code{mcmcsamp} has been withdrawn (or more precisely, ##' not updated to work with \code{lme4} versions >=1.0.0). ##' ##' Many users, including users of the \code{aovlmer.fnc} function ##' from the \code{languageR} package which relies on \code{mcmcsamp}, ##' will be deeply disappointed by this lacuna. Users who need p-values have ##' a variety of options: ##' \itemize{ ##' \item likelihood ratio tests via \code{anova} (MC,+) ##' \item profile confidence intervals via \code{\link{profile.merMod}} and \code{\link{confint.merMod}} (CI,+) ##' \item parametric bootstrap confidence intervals and model comparisons via \code{\link{bootMer}} (or \code{PBmodcomp} in the \code{pbkrtest} package) (MC/CI,*,+) ##' \item for random effects, simulation tests via the \code{RLRsim} package (MC,*) ##' \item for fixed effects, F tests via Kenward-Roger approximation using \code{KRmodcomp} from the \code{pbkrtest} package (MC) ##' \item \code{car::Anova} and \code{lmerTest::anova} provide wrappers for \code{pbkrtest}. \code{lmerTest::anova} also provides t tests via the Satterthwaite approximation (P,*) ##' } ##' In the list above, the methods marked \code{MC} provide explicit model comparisons; \code{CI} denotes confidence intervals; and \code{P} denotes parameter-level or sequential tests of all effects in a model. The starred (*) suggestions provide finite-size corrections (important when the number of groups is <50); those marked (+) support GLMMs as well as LMMs. ##' ##' When all else fails, don't forget to keep p-values in perspective: \url{http://www.phdcomics.com/comics/archive.php?comicid=905} ##' if(FALSE) ## C++ code in ../src/mcmcsamp.cpp -- is also #ifdef 0 # @S3method mcmcsamp merMod mcmcsamp.merMod <- function(object, n=1L, verbose=FALSE, saveb=FALSE, ...) { n <- max(1L, as.integer(n)[1]) dd <- getME(object, "devcomp")$dims ranef <- matrix(numeric(0), nrow = dd[["q"]], ncol = 0) if (saveb) ranef <- matrix(, nrow = dd[["q"]], ncol = n) sigma <- matrix(unname(sigma(object)), nrow = 1, ncol = (if (dd[["useSc"]]) n else 0)) ff <- fixef(object) fixef <- matrix(ff, nrow=dd[["p"]], ncol=n) rownames(fixef) <- names(ff) ## FIXME create a copy of the resp and pred modules ans <- new("merMCMC", Gp = object@Gp, # ST = matrix(.Call(mer_ST_getPars, object), dd[["np"]], n), call = object@call, dims = object@dims, deviance = rep.int(unname(object@deviance[["ML"]]), n), fixef = fixef, nc = sapply(object@ST, nrow), ranef = ranef, sigma = sigma) .Call(mer_MCMCsamp, ans, object) } lme4/R/optimizer.R0000644000176200001440000000613215022107260013456 0ustar liggesusers## --> ../man/NelderMead.Rd Nelder_Mead <- function(fn, par, lower=rep.int(-Inf, n), upper=rep.int(Inf, n), control=list()) { n <- length(par) if (is.null(xst <- control[["xst"]])) xst <- rep.int(0.02,n) if (is.null(xt <- control[["xt"]])) xt <- xst*5e-4 control[["xst"]] <- control[["xt"]] <- NULL ## mapping between simpler 'verbose' setting (0=no printing, 1=20, 2=10, 3=1) ## and internal 'iprint' control (frequency of printing) if (is.null(verbose <- control[["verbose"]])) verbose <- 0 control[["verbose"]] <- NULL if (is.null(control[["iprint"]])) { control[["iprint"]] <- switch(as.character(min(as.numeric(verbose),3L)), "0"=0, "1"=20,"2"=10,"3"=1) } stopifnot(is.function(fn), length(formals(fn)) == 1L, (n <- length(par <- as.numeric(par))) == length(lower <- as.numeric(lower)), length(upper <- as.numeric(upper)) == n, length(xst <- as.numeric(xst)) == n, all(xst != 0), length(xt <- as.numeric(xt)) == n) ## "NelderMead" reference class and constructor: --> ./AllClass.R : nM <- NelderMead$new(lower=lower, upper=upper, x0=par, xst=xst, xt=xt) cc <- do.call(function(iprint = 0L, maxfun = 10000L, FtolAbs = 1e-5, FtolRel = 1e-15, XtolRel = 1e-7, MinfMax= -.Machine$double.xmax, warnOnly=FALSE, ...) { if(...length() > 0) warning("unused control arguments ignored") list(iprint=iprint, maxfun=maxfun, FtolAbs=FtolAbs, FtolRel=FtolRel, XtolRel=XtolRel, MinfMax=MinfMax, warnOnly=warnOnly) }, control) nM$setFtolAbs(cc$FtolAbs) nM$setFtolRel(cc$FtolRel) nM$setIprint (cc$iprint) nM$setMaxeval(cc$maxfun) nM$setMinfMax(cc$MinfMax) it <- 0 repeat { it <- it + 1 nMres <- nM$newf(fn(nM$xeval())) if (nMres != 0L) break } cmsg <- "reached max evaluations" if (nMres == -4) { ## map max evals from error to warning cmsg <- warning(sprintf("failure to converge in %d evaluations",cc$maxfun)) nMres <- 4 } ## nMres: msgvec <- c("nm_forced", ## -3 "cannot generate a feasible simplex", ## -2 "initial x is not feasible", ## -1 "active", ## 0 (active) "objective function went below allowed minimum", ## 1 (minf_max) "objective function values converged to within tolerance", ## 2 (fcvg) "parameter values converged to within tolerance", ## 3 (xcvg) cmsg) if (nMres < 0) { ## i.e., in {-3, -2, -1} (if(cc$warnOnly) warning else stop)( msgvec[nMres+4] ) } list(fval = nM$value(), par = nM$xpos(), convergence = pmin(0, nMres), # positive nMres is also 'convergence' NM.result = nMres, `message` = msgvec[nMres+4], control = c(cc, xst=xst, xt=xt), feval = it) } lme4/R/AllClass.R0000644000176200001440000013331515022107260013136 0ustar liggesusers### Class definitions for the package ##' Class "lmList4" of 'lm' Objects on Common Model ##' --> ../man/lmList4-class.Rd ##' ~~~~~~~~~~~~~~~~~~~~~~~ ##' @keywords classes ##' @export setClass("lmList4", representation(call = "call", pool = "logical", groups = "ordered", # or "factor"? nlme does use ordered() origOrder = "integer" # a permutation ), contains = "list") ## TODO?: export setClass("lmList4.confint", contains = "array") forceCopy <- function(x) .Call(deepcopy, x) ### FIXME ### shouldn't we have "merPred" with two *sub* classes "merPredD" and "merPredS" ### for the dense and sparse X cases ? ## ## MM: _Or_ have "merPred" with X of class "mMatrix" := classUnion {matrix, Matrix} merPredD <- setRefClass("merPredD", # Predictor class for mixed-effects models with dense X fields = list(Lambdat = "dgCMatrix", # depends: theta and Lind ## = t(Lambda); Lambda := lower triangular relative variance factor LamtUt = "dgCMatrix", # depends: Lambdat and Ut Lind = "integer", # depends: nothing ## integer vector of the same length as 'x' slot in Lambdat. ## Its elements should be in 1: length(theta) Ptr = "externalptr", # depends: RZX = "matrix", # depends: lots Ut = "dgCMatrix", # depends: Zt and weights Utr = "numeric", # depends: lots V = "matrix", # depends: VtV = "matrix", Vtr = "numeric", X = "matrix", # model matrix for the fixed-effects parameters Xwts = "numeric", Zt = "dgCMatrix", # = t(Z); Z = sparse model matrix for the random effects beta0 = "numeric", delb = "numeric", delu = "numeric", theta = "numeric", # numeric vector of variance component parameters u0 = "numeric"), methods = list( initialize = function(X, Zt, Lambdat, Lind, theta, n, # = sample size, usually = nrow(X) ...) { if (!nargs()) return() ll <- list(...) X <<- as(X, "matrix") Zt <<- as(Zt, "dgCMatrix") Lambdat <<- as(Lambdat, "dgCMatrix") Lind <<- as.integer(Lind) theta <<- as.numeric(theta) N <- nrow(X) p <- ncol(X) q <- nrow(Zt) stopifnot(length(theta) > 0L, length(Lind) > 0L, all(sort(unique(Lind)) == seq_along(theta))) RZX <<- if (!is.null(ll$RZX)) array(ll$RZX, c(q, p)) else array(0, c(q, p)) Utr <<- if (!is.null(ll$Utr)) as.numeric(ll$Utr) else numeric(q) V <<- if (!is.null(ll$V)) array(ll$V, c(n, p)) else array(0, c(n, p)) VtV <<- if (!is.null(ll$VtV)) array(ll$VtV, c(p, p)) else array(0, c(p, p)) Vtr <<- if (!is.null(ll$Vtr)) as.numeric(ll$Vtr) else numeric(p) beta0 <<- if (!is.null(ll$beta0)) ll$beta0 else numeric(p) delb <<- if (!is.null(ll$delb)) as.numeric(ll$delb) else numeric(p) delu <<- if (!is.null(ll$delu)) as.numeric(ll$delu) else numeric(q) u0 <<- if (!is.null(ll$u0)) ll$u0 else numeric(q) Ut <<- if (n == N) Zt + 0 else Zt %*% sparseMatrix(i=seq_len(N), j=as.integer(gl(n, 1, N)), x=rep.int(1,N)) ## The following is a kludge to overcome problems when Zt is square ## by making LamtUt rectangular LtUt <- Lambdat %*% Ut ## if (nrow(LtUt) == ncol(LtUt)) ## LtUt <- cbind2(LtUt, ## sparseMatrix(i=integer(0), ## j=integer(0), ## x=numeric(0), ## dims=c(nrow(LtUt),1))) LamtUt <<- LtUt Xw <- ll$Xwts ## list(...)$Xwts Xwts <<- if (is.null(Xw)) rep.int(1, N) else as.numeric(Xw) initializePtr() }, CcNumer = function() { 'returns the numerator of the orthogonality convergence criterion' .Call(merPredDCcNumer, ptr()) }, L = function() { 'returns the current value of the sparse Cholesky factor' .Call(merPredDL, ptr()) }, P = function() { 'returns the permutation vector for the sparse Cholesky factor' .Call(merPredDPvec, ptr()) }, RX = function() { 'returns the dense downdated Cholesky factor for the fixed-effects parameters' .Call(merPredDRX, ptr()) }, RXi = function() { 'returns the inverse of the dense downdated Cholesky factor for the fixed-effects parameters' .Call(merPredDRXi, ptr()) }, RXdiag = function() { 'returns the diagonal of the dense downdated Cholesky factor' .Call(merPredDRXdiag, ptr()) }, b = function(fac) { 'random effects on original scale for step factor fac' .Call(merPredDb, ptr(), as.numeric(fac)) }, beta = function(fac) { 'fixed-effects coefficients for step factor fac' .Call(merPredDbeta, ptr(), as.numeric(fac)) }, copy = function(shallow = FALSE) { def <- .refClassDef selfEnv <- as.environment(.self) vEnv <- new.env(parent=emptyenv()) for (field in setdiff(names(def@fieldClasses), "Ptr")) { if (shallow) assign(field, get(field, envir = selfEnv), envir = vEnv) else { current <- get(field, envir = selfEnv) if (is(current, "envRefClass")) current <- current$copy(FALSE) assign(field, forceCopy(current), envir = vEnv) } } do.call(merPredD$new, c(as.list(vEnv), n=nrow(vEnv$V), Class=def)) }, ldL2 = function() { 'twice the log determinant of the sparse Cholesky factor' .Call(merPredDldL2, ptr()) }, ldRX2 = function() { 'twice the log determinant of the downdated dense Cholesky factor' .Call(merPredDldRX2, ptr()) }, unsc = function() { 'the unscaled variance-covariance matrix of the fixed-effects parameters' .Call(merPredDunsc, ptr()) }, linPred = function(fac) { 'evaluate the linear predictor for step factor fac' .Call(merPredDlinPred, ptr(), as.numeric(fac)) }, installPars = function(fac) { 'update u0 and beta0 to the values for step factor fac' .Call(merPredDinstallPars, ptr(), as.numeric(fac)) }, initializePtr = function() { Ptr <<- .Call(merPredDCreate, as(X, "matrix"), Lambdat, LamtUt, Lind, RZX, Ut, Utr, V, VtV, Vtr, Xwts, Zt, beta0, delb, delu, theta, u0) .Call(merPredDsetTheta, Ptr, theta) .Call(merPredDupdateXwts, Ptr, Xwts) .Call(merPredDupdateDecomp, Ptr, NULL) }, ptr = function() { 'returns the external pointer, regenerating if necessary' if (length(theta)) { if (.Call(isNullExtPtr, Ptr)) initializePtr() } Ptr }, setBeta0 = function(beta0) { 'install a new value of beta' .Call(merPredDsetBeta0, ptr(), as.numeric(beta0)) }, setTheta = function(theta) { 'install a new value of theta' .Call(merPredDsetTheta, ptr(), as.numeric(theta)) }, setZt = function(ZtNonZero) { 'install new values in Zt' .Call(merPredDsetZt, ptr(), as.numeric(ZtNonZero)) }, solve = function() { 'solve for the coefficient increments delu and delb' .Call(merPredDsolve, ptr()) }, solveU = function() { 'solve for the coefficient increment delu only (beta is fixed)' .Call(merPredDsolveU, ptr()) }, setDelu = function(val) { 'set the coefficient increment delu' .Call(merPredDsetDelu , ptr(), as.numeric(val)) }, setDelb = function(val) { 'set the coefficient increment delb' .Call(merPredDsetDelb , ptr(), as.numeric(val)) }, sqrL = function(fac) { 'squared length of u0 + fac * delu' .Call(merPredDsqrL, ptr(), as.numeric(fac)) }, u = function(fac) { 'orthogonal random effects for step factor fac' .Call(merPredDu, ptr(), as.numeric(fac)) }, updateDecomp = function(XPenalty = NULL) { 'update L, RZX and RX from Ut, Vt and VtV' invisible(.Call(merPredDupdateDecomp, ptr(), XPenalty)) }, updateL = function() { 'update LamtUt and L' .Call(merPredDupdateL, ptr()) }, updateLamtUt = function() { 'update LamtUt and L' .Call(merPredDupdateLamtUt, ptr()) }, updateRes = function(wtres) { 'update Vtr and Utr using the vector of weighted residuals' .Call(merPredDupdateRes, ptr(), as.numeric(wtres)) }, updateXwts = function(wts) { 'update Ut and V from Zt and X using X weights' .Call(merPredDupdateXwts, ptr(), wts) } ) ) merPredD$lock("Lambdat", "LamtUt", "Lind", "RZX", "Ut", "Utr", "V", "VtV", "Vtr", "X", "Xwts", "Zt", "beta0", "delb", "delu", "theta", "u0") ## -> ../man/lmResp-class.Rd ## ~~~~~~~~~~~~~~~~~~~~~~ lmResp <- # base class for response modules setRefClass("lmResp", fields = list(Ptr = "externalptr", mu = "numeric", offset = "numeric", sqrtXwt = "numeric", sqrtrwt = "numeric", weights = "numeric", wtres = "numeric", y = "numeric"), methods = list( allInfo = function() { 'return all the information available on the object' data.frame(y=y, offset=offset, weights=weights, mu=mu, rwt=sqrtrwt, wres=wtres, Xwt=sqrtXwt) }, initialize = function(...) { if (!nargs()) return() ll <- list(...) if (is.null(ll$y)) stop("y must be specified") y <<- as.numeric(ll$y) n <- length(y) mu <<- if (!is.null(ll[["mu"]])) as.numeric(ll[["mu"]]) else numeric(n) offset <<- if (!is.null(ll$offset)) as.numeric(ll$offset) else numeric(n) weights <<- if (!is.null(ll$weights)) as.numeric(ll$weights) else rep.int(1,n) sqrtXwt <<- if (!is.null(ll$sqrtXwt)) as.numeric(ll$sqrtXwt) else sqrt(weights) sqrtrwt <<- if (!is.null(ll$sqrtrwt)) as.numeric(ll$sqrtrwt) else sqrt(weights) wtres <<- sqrtrwt * (y - mu) }, copy = function(shallow = FALSE) { def <- .refClassDef selfEnv <- as.environment(.self) vEnv <- new.env(parent=emptyenv()) for (field in setdiff(names(def@fieldClasses), "Ptr")) { if (shallow) assign(field, get(field, envir = selfEnv), envir = vEnv) else { current <- get(field, envir = selfEnv) if (is(current, "envRefClass")) current <- current$copy(FALSE) ## deep-copy hack +0 assign(field, forceCopy(current), envir = vEnv) } } do.call(new, c(as.list(vEnv), Class=def)) }, initializePtr = function() { Ptr <<- .Call(lm_Create, y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres) .Call(lm_updateMu, Ptr, mu) }, ptr = function() { 'returns the external pointer, regenerating if necessary' if (length(y)) { if (.Call(isNullExtPtr, Ptr)) initializePtr() } Ptr }, setOffset = function(oo) { 'change the offset in the model (used in profiling)' .Call(lm_setOffset, ptr(), as.numeric(oo)) }, setResp = function(rr) { 'change the response in the model, usually after a deep copy' .Call(lm_setResp, ptr(), as.numeric(rr)) }, setWeights = function(ww) { 'change the prior weights in the model' .Call(lm_setWeights, ptr(), as.numeric(ww)) }, updateMu = function(gamma) { 'update mu, wtres and wrss from the linear predictor' .Call(lm_updateMu, ptr(), as.numeric(gamma)) }, wrss = function() { 'returns the weighted residual sum of squares' .Call(lm_wrss, ptr()) }) ) lmResp$lock("mu", "offset", "sqrtXwt", "sqrtrwt", "weights", "wtres")#, "y") lmerResp <- setRefClass("lmerResp", contains = "lmResp", fields = list(REML = "integer"), methods= list(initialize = function(...) { REML <<- as.integer(list(...)$REML) if (length(REML) != 1L) REML <<- 0L callSuper(...) }, initializePtr = function() { Ptr <<- .Call(lmer_Create, y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres) .Call(lm_updateMu, Ptr, mu - offset) .Call(lmer_setREML, Ptr, REML) }, ptr = function() { 'returns the external pointer, regenerating if necessary' if (length(y)) if (.Call(isNullExtPtr, Ptr)) initializePtr() Ptr }, objective = function(ldL2, ldRX2, sqrL, sigma.sq = NULL) { 'returns the profiled deviance or REML criterion' .Call(lmer_Laplace, ptr(), ldL2, ldRX2, sqrL, sigma.sq) }) ) setOldClass("family") ##' @export glmResp <- setRefClass("glmResp", contains = "lmResp", fields = list(eta = "numeric", family = "family", n = "numeric"), methods= list(initialize = function(...) { callSuper(...) ll <- list(...) if (is.null(ll$family)) stop("family must be specified") family <<- ll$family n <<- if (!is.null(ll$n)) as.numeric(ll$n) else rep.int(1,length(y)) eta <<- if (!is.null(e <- ll[["etastart"]])) as.numeric(e) else numeric(length(y)) }, aic = function() { .Call(glm_aic, ptr()) }, allInfo = function() { 'return all the information available on the object' cbind(callSuper(), data.frame(eta=eta, muEta=muEta(), var=variance(), WrkWt=sqrtWrkWt(), wrkRes=wrkResids(), wrkResp=wrkResp(), devRes=devResid())) }, devResid = function() { 'returns the vector of deviance residuals' .Call(glm_devResid, ptr()) }, fam = function() { 'returns the name of the glm family' .Call(glm_family, ptr()) }, Laplace = function(ldL2, ldRX2, sqrL) { 'returns the Laplace approximation to the profiled deviance' .Call(glm_Laplace, ptr(), ldL2, ldRX2, sqrL) }, link = function() { 'returns the name of the glm link' .Call(glm_link, ptr()) }, muEta = function() { 'returns the diagonal of the Jacobian matrix, d mu/d eta' .Call(glm_muEta, ptr()) }, ptr = function() { 'returns the external pointer, regenerating if necessary' if (length(y)) { if (.Call(isNullExtPtr, Ptr)) { Ptr <<- .Call(glm_Create, family, y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres, eta, n) .Call(glm_updateMu, Ptr, eta - offset) } } Ptr }, resDev = function() { 'returns the sum of the deviance residuals' .Call(glm_resDev, ptr()) }, setTheta = function(theta) { 'sets a new value of theta, for negative binomial distribution only' .Call(glm_setTheta, ptr(), as.numeric(theta)) }, sqrtWrkWt = function() { 'returns the square root of the working X weights' .Call(glm_sqrtWrkWt, ptr()) }, theta = function() { 'query the value of theta, for negative binomial distribution only' .Call(glm_theta, ptr()) }, updateMu = function(gamma) { 'update mu, residuals, weights, etc. from the linear predictor' .Call(glm_updateMu, ptr(), as.numeric(gamma)) }, updateWts = function() { 'update the residual and X weights from the current value of eta' .Call(glm_updateWts, ptr()) }, variance = function() { 'returns the vector of variances' .Call(glm_variance, ptr()) }, wtWrkResp = function() { 'returns the vector of weighted working responses' .Call(glm_wtWrkResp, ptr()) }, wrkResids = function() { 'returns the vector of working residuals' .Call(glm_wrkResids, ptr()) }, wrkResp = function() { 'returns the vector of working responses' .Call(glm_wrkResp, ptr()) } ) ) glmResp$lock("family", "n", "eta") ##' @export nlsResp <- setRefClass("nlsResp", contains = "lmResp", fields= list(gam = "numeric", nlmod = "formula", nlenv = "environment", pnames= "character"), methods= list(initialize = function(...) { callSuper(...) ll <- list(...) if (is.null(ll$nlmod)) stop("nlmod must be specified") nlmod <<- ll$nlmod if (is.null(ll$nlenv)) stop("nlenv must be specified") nlenv <<- ll$nlenv if (is.null(ll$pnames)) stop("pnames must be specified") pnames <<- ll$pnames if (is.null(ll$gam)) stop("gam must be specified") stopifnot(length(ll$gam) == length(offset)) gam <<- ll$gam }, Laplace =function(ldL2, ldRX2, sqrL) { 'returns the profiled deviance or REML criterion' .Call(nls_Laplace, ptr(), ldL2, ldRX2, sqrL) }, ptr = function() { 'returns the external pointer, regenerating if necessary' if (length(y)) { if (.Call(isNullExtPtr, Ptr)) { Ptr <<- .Call(nls_Create, y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres, gam, nlmod[[2]], nlenv, pnames) .Call(nls_updateMu, Ptr, gam) } } Ptr }, updateMu=function(gamma) { 'update mu, residuals, gradient, etc. given the linear predictor matrix' .Call(nls_updateMu, ptr(), as.numeric(gamma)) }) ) nlsResp$lock("nlmod", "nlenv", "pnames") ##' Generator object for the \code{\linkS4class{glmFamily}} class ##' ##' The generator object for the \code{\linkS4class{glmFamily}} reference class. ##' Such an object is primarily used through its \code{new} method. ##' ##' ##' @param ... Named argument (see Note below) ##' @note Arguments to the \code{new} method must be named arguments. ##' @section Methods: \describe{ ##' \item{\code{new(family=family)}}{Create a new ##' \code{\linkS4class{glmFamily}} object} ##' } ##' @seealso \code{\linkS4class{glmFamily}} ##' @keywords classes ##' @export glmFamily <- # used in tests of family definitions setRefClass("glmFamily", fields=list(Ptr="externalptr", family="family"), methods= list( aic = function(y, n, mu, wt, dev) { 'returns the value from the aic member function, which is actually the deviance' nn <- length(y <- as.numeric(y)) stopifnot(length(n <- as.numeric(n)) == nn, length(mu <- as.numeric(mu)) == nn, length(wt <- as.numeric(wt)) == nn, all(wt >= 0), length(dev <- as.numeric(dev)) == 1L) .Call(glmFamily_aic, ptr(), y, n, mu, wt, dev) }, devResid = function(y, mu, wt) { 'applies the devResid function to y, mu and wt' mu <- as.numeric(mu) wt <- as.numeric(wt) y <- as.numeric(y) stopifnot(length(mu) == length(wt), length(mu) == length(y), all(wt >= 0)) .Call(glmFamily_devResid, ptr(), y, mu, wt) }, link = function(mu) { 'applies the (forward) link function to mu' .Call(glmFamily_link, ptr(), as.numeric(mu)) }, linkInv = function(eta) { 'applies the inverse link function to eta' .Call(glmFamily_linkInv, ptr(), as.numeric(eta)) }, muEta = function(eta) { 'applies the muEta function to eta' .Call(glmFamily_muEta, ptr(), as.numeric(eta)) }, ptr = function() { if (length(family)) if (.Call(isNullExtPtr, Ptr)) Ptr <<- .Call(glmFamily_Create, family) Ptr }, setTheta = function(theta) { 'sets a new value of theta, for negative binomial distribution only' .Call(glmFamily_setTheta, ptr(), as.numeric(theta)) }, theta = function() { 'query the value of theta, for negative binomial distribution only' .Call(glmFamily_theta, ptr()) }, variance = function(mu) { 'applies the variance function to mu' .Call(glmFamily_variance, ptr(), as.numeric(mu)) }) ) ##' Class \code{"glmFamily"} - a reference class for \code{\link{family}} ##' ##' This class is a wrapper class for \code{\link{family}} objects specifying a ##' distibution family and link function for a generalized linear model ##' (\code{\link{glm}}). The reference class contains an external pointer to a ##' C++ object representing the class. For common families and link functions ##' the functions in the family are implemented in compiled code so they can be ##' accessed from other compiled code and for a speed boost. ##' ##' ##' @name glmFamily-class ##' @docType class ##' @note Objects from this reference class correspond to objects in a C++ ##' class. Methods are invoked on the C++ class using the external pointer in ##' the \code{Ptr} field. When saving such an object the external pointer is ##' converted to a null pointer, which is why there is a redundant field ##' \code{ptr} that is an active-binding function returning the external ##' pointer. If the \code{Ptr} field is a null pointer, the external pointer is ##' regenerated for the stored \code{family} field. ##' @section Extends: All reference classes extend and inherit methods from ##' \code{"\linkS4class{envRefClass}"}. ##' @seealso \code{\link{family}}, \code{\link{glmFamily}} ##' @keywords classes ##' @examples ##' ##' str(glmFamily$new(family=poisson())) NULL ##' Generator object for the golden search optimizer class. ##' ##' The generator objects for the \code{\linkS4class{golden}} class of a scalar ##' optimizer for a parameter within an interval. The optimizer uses reverse ##' communications. ##' ##' @param \dots additional, optional arguments. None are used at present. ##' @note Arguments to the \code{new} methods must be named arguments. ##' \code{lower} and \code{upper} are the bounds for the scalar parameter; they must be finite. ##' @section Methods: ##' \describe{ ##' \item{\code{new(lower=lower, upper=upper)}}{Create a new ##' \code{\linkS4class{golden}} object.} ##' } ##' @seealso \code{\linkS4class{golden}} ##' @keywords classes ##' @export golden <- setRefClass("golden", # Reverse communication implementation of Golden Search fields = list( Ptr = "externalptr", lowerbd = "numeric", upperbd = "numeric" ), methods = list( initialize = function(lower, upper, ...) { stopifnot(length(lower <- as.numeric(lower)) == 1L, length(upper <- as.numeric(upper)) == 1L, lower > -Inf, upper < Inf, lower < upper) lowerbd <<- lower upperbd <<- upper Ptr <<- .Call(golden_Create, lower, upper) }, ptr = function() { if (length(lowerbd)) if (.Call(isNullExtPtr, Ptr)) Ptr <<- .Call(golden_Create, lowerbd, upperbd) Ptr }, newf = function(value) { stopifnot(length(value <- as.numeric(value)) == 1L) .Call(golden_newf, ptr(), value) }, value = function() .Call(golden_value, ptr()), xeval = function() .Call(golden_xeval, ptr()), xpos = function() .Call(golden_xpos, ptr()) ) ) ##' Class \code{"golden"} ##' ##' A reference class for a golden search scalar optimizer using reverse ##' communication. ##' ##' ##' @name golden-class ##' @docType class ##' @section Extends: All reference classes extend and inherit methods from ##' \code{"\linkS4class{envRefClass}"}. ##' @keywords classes ##' @examples ##' ##' showClass("golden") ##' NULL ## Generator object for the Nelder-Mead optimizer class "NelderMead" ## ## A reference class for a Nelder-Mead simplex optimizer allowing box ## constraints on the parameters and using reverse communication. NelderMead <- setRefClass("NelderMead", # Reverse communication implementation of Nelder-Mead simplex optimizer fields = list( Ptr = "externalptr", lowerbd = "numeric", upperbd = "numeric", xstep = "numeric", xtol = "numeric" ), methods = list( initialize = function(lower, upper, xst, x0, xt, ...) { stopifnot((n <- length(lower <- as.numeric(lower))) > 0L, length(upper <- as.numeric(upper)) == n, all(lower < upper), length(xst <- as.numeric(xst)) == n, all(xst != 0), length(x0 <- as.numeric(x0)) == n, all(x0 >= lower), all(x0 <= upper), all(is.finite(x0)), length(xt <- as.numeric(xt)) == n, all(xt > 0)) lowerbd <<- lower upperbd <<- upper xstep <<- xst xtol <<- xt Ptr <<- .Call(NelderMead_Create, lowerbd, upperbd, xstep, x0, xtol) }, ptr = function() { if (length(lowerbd)) if (.Call(isNullExtPtr, Ptr)) Ptr <<- .Call(NelderMead_Create, lowerbd, upperbd, xstep, x0, xtol) Ptr }, newf = function(value) { stopifnot(length(value <- as.numeric(value)) == 1L) .Call(NelderMead_newf, ptr(), value) }, setForceStop = function(stp=TRUE) .Call(NelderMead_setForce_stop, ptr(), stp), setFtolAbs = function(fta) .Call(NelderMead_setFtol_abs, ptr(), fta), setFtolRel = function(ftr) .Call(NelderMead_setFtol_rel, ptr(), ftr), setMaxeval = function(mxev) .Call(NelderMead_setMaxeval, ptr(), mxev), setMinfMax = function(minf) .Call(NelderMead_setMinf_max, ptr(), minf), setIprint = function(iprint) .Call(NelderMead_setIprint, ptr(), iprint), value = function() .Call(NelderMead_value, ptr()), xeval = function() .Call(NelderMead_xeval, ptr()), xpos = function() .Call(NelderMead_xpos, ptr()) ) ) ##' Class "merMod" of Fitted Mixed-Effect Models ##' ##' A mixed-effects model is represented as a \code{\linkS4class{merPredD}} object ##' and a response module of a class that inherits from class ##' \code{\linkS4class{lmResp}}. A model with a \code{\linkS4class{lmerResp}} ##' response has class \code{lmerMod}; a \code{\linkS4class{glmResp}} response ##' has class \code{glmerMod}; and a \code{\linkS4class{nlsResp}} response has ##' class \code{nlmerMod}. ##' ##' @name merMod-class ##' @aliases merMod-class lmerMod-class glmerMod-class nlmerMod-class merMod ##' show,merMod-method ##' anova.merMod coef.merMod deviance.merMod ##' fitted.merMod formula.merMod logLik.merMod ##' model.frame.merMod model.matrix.merMod print.merMod ##' show.merMod summary.merMod ##' terms.merMod update.merMod ##' vcov.merMod print.summary.merMod show.summary.merMod ##' summary.summary.merMod vcov.summary.merMod ##' @docType class ##' @section Objects from the Class: Objects are created by calls to ##' \code{\link{lmer}}, \code{\link{glmer}} or \code{\link{nlmer}}. ##' @seealso \code{\link{lmer}}, \code{\link{glmer}}, \code{\link{nlmer}}, ##' \code{\linkS4class{merPredD}}, \code{\linkS4class{lmerResp}}, ##' \code{\linkS4class{glmResp}}, \code{\linkS4class{nlsResp}} ##' @keywords classes ##' @examples ##' ##' showClass("merMod") ##' methods(class="merMod") ##' @export setClass("merMod", representation(Gp = "integer", call = "call", frame = "data.frame", # "model.frame" is not S4-ized yet flist = "list", cnms = "list", lower = "numeric", theta = "numeric", beta = "numeric", u = "numeric", devcomp = "list", pp = "merPredD", optinfo = "list")) ##' @export setClass("lmerMod", representation(resp="lmerResp"), contains="merMod") ##' @export setClass("glmerMod", representation(resp="glmResp"), contains="merMod") ##' @export setClass("nlmerMod", representation(resp="nlsResp"), contains="merMod") ##' Generator object for the rePos (random-effects positions) class ##' ##' The generator object for the \code{\linkS4class{rePos}} class used ##' to determine the positions and orders of random effects associated ##' with particular random-effects terms in the model. ##' @param \dots Argument list (see Note). ##' @note Arguments to the \code{new} methods must be named arguments. ##' \code{mer}, an object of class \code{"\linkS4class{merMod}"}, is ##' the only required/expected argument. ##' @section Methods: ##' \describe{ ##' \item{\code{new(mer=mer)}}{Create a new ##' \code{\linkS4class{rePos}} object.} ##' } ##' @seealso \code{\linkS4class{rePos}} ##' @keywords classes ##' @export rePos <- setRefClass("rePos", fields = list( cnms = "list", # component names (components are terms within a RE term) flist = "list", # list of grouping factors used in the random-effects terms ncols = "integer", # number of components for each RE term nctot = "integer", # total number of components per factor nlevs = "integer", # number of levels for each unique factor offsets = "integer", # points to where each term starts terms = "list" # list with one element per factor, indicating corresponding term ), methods = list( initialize = function(mer, ...) { ##' asgn indicates unique elements of flist ##' stopifnot((ntrms <- length(Cnms <- mer@cnms)) > 0L, (length(Flist <- mer@flist)) > 0L, length(asgn <- as.integer(attr(Flist, "assign"))) == ntrms) cnms <<- Cnms flist <<- Flist ncols <<- unname(lengths(cnms)) nctot <<- unname(as.vector(tapply(ncols, asgn, sum))) nlevs <<- unname(vapply(flist, function(el) length(levels(el)), 0L)) # why not replace the sapply with ncols*nlevs[asgn] ?? offsets <<- c(0L, cumsum(sapply(seq_along(asgn), function(i) ncols[i] * nlevs[asgn[i]]))) terms <<- lapply(seq_along(flist), function(i) which(asgn == i)) } ) ) ##' Class \code{"rePos"} ##' ##' A reference class for determining the positions in the random-effects vector ##' that correspond to particular random-effects terms in the model formula ##' ##' @name rePos-class ##' @docType class ##' @section Extends: All reference classes extend and inherit methods from ##' \code{"\linkS4class{envRefClass}"}. ##' @keywords classes ##' @examples ##' ##' showClass("rePos") ##' rePos$lock("cnms", "flist", "ncols", "nctot", "nlevs", "terms") vcRep <- setRefClass("vcRep", fields = list( theta = "numeric", lower = "numeric", Lambdat = "dgCMatrix", Lind = "integer", Gp = "integer", flist = "list", cnms = "list", ncols = "integer", nctot = "integer", nlevs = "integer", offsets = "integer", terms = "list", sig = "numeric", nms = "character", covar = "list", useSc = "logical" ), methods = list( initialize = function(mer, ...) { stopifnot((ntrms <- length(Cnms <- mer@cnms)) > 0L, (length(Flist <- mer@flist)) > 0L, length(asgn <- as.integer(attr(Flist, "assign"))) == ntrms) lower <<- getME(mer, "lower") theta <<- getME(mer, "theta") Lambdat <<- getME(mer, "Lambdat") Lind <<- getME(mer, "Lind") Gp <<- getME(mer, "Gp") cnms <<- Cnms flist <<- Flist ncols <<- unname(lengths(cnms)) nctot <<- unname(as.vector(tapply(ncols, asgn, sum))) nlevs <<- unname(vapply(flist, function(el) length(levels(el)), 0L)) offsets <<- c(0L, cumsum(sapply(seq_along(asgn), function(i) ncols[i] * nlevs[asgn[i]]))) terms <<- lapply(seq_along(Flist), function(i) which(asgn == i)) sig <<- sigma(mer) nms <<- names(Flist)[asgn] covar <<- mkVarCorr(sig, cnms, ncols, theta, nms) useSc <<- as.logical(getME(mer, "devcomp")$dims['useSc']) }, asCovar = function() { ans <- lapply(covar, function(x) { attr(x, "correlation") <- attr(x, "stddev") <- NULL x }) attr(ans, "residVar") <- attr(covar, "sc")^2 ans }, asCorr = function() { ans <- lapply(covar, function(x) list(correlation=attr(x, "correlation"), stddev=attr(x, "stddev"))) attr(ans, "residSD") <- attr(covar, "sc") ans }, setTheta = function(ntheta) { stopifnot(length(ntheta <- as.numeric(ntheta)) == length(lower), all(ntheta >= lower)) theta <<- ntheta covar <<- mkVarCorr(sig, cnms, ncols, theta, nms) }, setSc = function(nSc) { stopifnot(useSc, length(nSc <- as.numeric(nSc)) == 1L) sig <<- nSc covar <<- mkVarCorr(sig, cnms, ncols, theta, nms) }, setResidVar = function(nVar) setSc(sqrt(as.numeric(nVar))), setRECovar = function(CV) { if (is.matrix(CV) && length(covar) == 1L) { CV <- list(CV) names(CV) <- names(covar) } covsiz <- sapply(covar, ncol) stopifnot(is.list(CV), all(names(CV) == names(covar)), all(sapply(CV, isSymmetric)), all(sapply(CV, ncol) == covsiz)) if (!all(lengths(cnms) == covsiz)) error("setRECovar currently requires distinct grouping factors") theta <<- sapply(CV, function(mm) { ff <- t(chol(mm))/sig ff[upper.tri(ff, diag=TRUE)] }) }) ) lme4/R/simulate.formula.R0000644000176200001440000001317315022107260014726 0ustar liggesusers## NOTE: Unlike the rest of the package, the functions in this file ## are licensed under the MIT License to encourage incorporation. ## ## Copyright (c) 2020 Pavel N. Krivitsky and Benjamin Bolker ## ## Permission is hereby granted, free of charge, to any person obtaining a copy ## of this software and associated documentation files (the "Software"), to deal ## in the Software without restriction, including without limitation the rights ## to use, copy, modify, merge, publish, distribute, sublicense, and/or sell ## copies of the Software, and to permit persons to whom the Software is ## furnished to do so, subject to the following conditions: ## ## The above copyright notice and this permission notice shall be included in all ## copies or substantial portions of the Software. ## ## THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR ## IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, ## FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE ## AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER ## LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, ## OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE ## SOFTWARE. ## #' A `simulate` Method for `formula` objects that dispatches based on the Left-Hand Side #' #' This method evaluates the left-hand side (LHS) of the given formula and #' dispatches it to an appropriate method based on the result by #' setting an nonce class name on the formula. #' #' @param object a one- or two-sided [`formula`]. #' @param nsim,seed number of realisations to simulate and the random #' seed to use; see [simulate()]. #' @param ... additional arguments to methods. #' @param basis if given, overrides the LHS of the formula for the #' purposes of dispatching. #' @param newdata,data if passed, the `object`'s LHS is evaluated in #' this environment; at most one of the two may be passed. #' #' The dispatching works as follows: #' #' 1. If `basis` is not passed, and the formula has an LHS the #' expression on the LHS of the formula in the `object` is #' evaluated in the environment `newdata` or `data` (if given), in #' any case enclosed by the environment of `object`. Otherwise, #' `basis` is used. #' #' 1. The result is set as an attribute `".Basis"` on `object`. If #' there is no `basis` or LHS, it is not set. #' #' 1. The class vector of `object` has `c("formula_lhs_\var{CLASS}", #' "formula_lhs")` prepended to it, where \var{CLASS} is the class #' of the LHS value or `basis`. If LHS or `basis` has multiple #' classes, they are all prepended; if there is no LHS or `basis`, #' `c("formula_lhs_", "formula_lhs")` is. #' #' 1. [simulate()] generic is evaluated on the new `object`, with all #' arguments passed on, excluding `basis`; if `newdata` or `data` #' are missing, they too are not passed on. The evaluation takes #' place in the parent's environment. #' #' A "method" to receive a formula whose LHS evaluates to \var{CLASS} #' can therefore be implemented by a function #' `simulate.formula_lhs_\var{CLASS}()`. This function can expect a #' [`formula`] object, with additional attribute `.Basis` giving the #' evaluated LHS (so that it does not need to be evaluated again). #' #' @export ## See https://github.com/lme4/lme4/issues/566 for further discussion simulate.formula <- function(object, nsim=1, seed=NULL, ..., basis, newdata, data) { ## utility fun for generating new class cfun <- function(cc) { c(paste0("formula_lhs_", cc), "formula_lhs", class(object)) } ## grab the arguments and the call and replace the function to be called with stats::simulate cl <- match.call() cl[[1L]] <- quote(stats::simulate) if (length(object)==3 || !missing(basis)) { ## two-sided formula or basis given if (missing(basis)) { # If basis is not passed, evaluate LHS. if (!missing(data) && !missing(newdata)) stop("At most one of ", sQuote("data"), " or ", sQuote("newdata"), " can be specified.") evaldata <- if (!missing(data)) data else if(!missing(newdata)) newdata else environment(object) lhs <- object[[2L]] .Basis <- try(eval(lhs, envir=evaldata, enclos=environment(object)), silent=TRUE) if (inherits(.Basis,"try-error")) { ## can't evaluate LHS stop(simpleError(paste("Error evaluating the left-hand side of the formula:", .Basis))) } } else { # Otherwise, override LHS. .Basis <- basis } ## Set the basis object and class. attr(object,".Basis") <- .Basis class(object) <- cfun(class(.Basis)) } else { ## one-sided class(object) <- cfun("") } ## Replace the dispatched-on argument (object) with the updated formula. cl[["object"]] <- object ## If data argument was not actually passed, remove it from the call. if(missing(data)) cl <- cl[names(cl)!="data"] ## If newdata argument was not actually passed, remove it from the call. if(missing(newdata)) cl <- cl[names(cl)!="newdata"] ## Always remove basis from the call (since it's an attribute of object now). cl <- cl[names(cl)!="basis"] # Evaluate the modified call as if in the environment from which simulate.formula() was called. (A poor man's NextMethod().) eval(cl, parent.frame()) } #' @describeIn simulate.formula A function to catch the situation when there is no method implemented for the class to which the LHS evaluates. #' #' @export simulate.formula_lhs <- function(object, nsim=1, seed=NULL, ...){ stop("No applicable method for LHS of type ", paste0(sQuote(class(attr(object, ".Basis"))), collapse=", "), ".") } lme4/R/lmList.R0000644000176200001440000004333715103163201012705 0ustar liggesusers## List of linear models according to a grouping factor ## Extract the model formula modelFormula <- function(form) { if (!inherits(form, "formula") || length(form) != 3) stop("formula must be a two-sided formula object") rhs <- form[[3]] if (!inherits(rhs, "call") || rhs[[1]] != as.symbol('|')) stop("rhs of formula must be a conditioning expression") form[[3]] <- rhs[[2]] list(model = dropOffset(form), groups = rhs[[3]]) } dropOffset <- function(form) { ## atomic if (is.symbol(form) || is.numeric(form)) return(form) ## binary if (identical(form[[1]],quote(offset))) { NULL } else { ## unary operator if (length(form)==2) { form[[2]] <- dropOffset(form[[2]]) return(form) } nb2 <- dropOffset(form[[2]]) nb3 <- dropOffset(form[[3]]) if (is.null(nb2)) nb3 else if (is.null(nb3)) nb2 else { form[[2]] <- nb2 form[[3]] <- nb3 return(form) } } } ## dropOffset(y~x) ## dropOffset(y~x+offset(stuff)) ## dropOffset(y~-x+offset(stuff)) ## dropOffset(~-x+offset(stuff)) if(getRversion() < "3.5.0") { ##' Utility for lmList(), ...: Collect errors from a list \code{x}, ##' produce a "summary warning" and keep that message as "warningMsg" attribute warnErrList <- function(x, warn = TRUE) { errs <- vapply(x, inherits, NA, what = "error") if (any(errs)) { v.err <- x[errs] e.call <- paste(deparse(conditionCall(v.err[[1]])), collapse = "\n") tt <- table(vapply(v.err, conditionMessage, "")) msg <- if(length(tt) == 1) sprintf(ngettext(tt[[1]], "%d error caught in %s: %s", "%d times caught the same error in %s: %s"), tt[[1]], e.call, names(tt)[[1]]) else ## at least two different errors caught paste(gettextf( "%d errors caught in %s. The error messages and their frequencies are", sum(tt), e.call), paste(capture.output(sort(tt)), collapse="\n"), sep="\n") if(warn) warning(msg, call. = FALSE, domain = NA) x[errs] <- list(NULL) attr(x, "warningMsg") <- msg } x } }# R <= 3.4.x ##' @title List of lm Objects with a Common Model ##' @param formula a linear formula object of the form ##' \code{y ~ x1+...+xn | g}. In the formula object, \code{y} represents ##' the response, \code{x1,...,xn} the covariates, and \code{g} the ##' grouping factor specifying the partitioning of the data according to ##' which different \code{lm} fits should be performed. ##' @inheritParams lmer ##' @param family an optional family specification for a generalized ##' linear model. ##' @param pool logical scalar, should the variance estimate pool the ##' residual sums of squares ##' @param ... additional, optional arguments to be passed to the ##' model function or family evaluation. ##' @export lmList <- function(formula, data, family, subset, weights, na.action, offset, pool = !isGLM || .hasScale(family2char(family)), warn = TRUE, ...) { stopifnot(inherits(formula, "formula")) ## model.frame(groupedData) was problematic ... but not as we ## are currently using it. mCall <- mf <- match.call() ## MM: I had this (instead of below (inherited from nlme?)): ## if(!missing(subset)) ## data <- data[eval(asOneSidedFormula(mf[["subset"]])[[2]], data),, drop = FALSE] ## in contrast to the usual R model-fitting idiom, we do **not** ## want to evaluate the model frame here; it will mess up any derived ## variables we have when we go to fit the sub-models. We were previously ## using model.frame() on the entire data set, but that does not ## exclude unused columns ... and hence screws us up when there are ## NA values in unused columns. All we need the model frame for ## is evaluating the groups. ## keep weights and offsets in case we have NAs there?? m <- match(c("formula", "data", "subset", "na.action", "weights", "offset"), names(mf), 0) mf <- mf[c(1, m)] ## substitute `+' for `|' in the formula mf$formula <- reformulas::subbars(formula) mf$drop.unused.levels <- TRUE ## pass NAs for now -- want *all* groups, weights, offsets recovered mf$na.action <- na.pass mf[[1L]] <- quote(stats::model.frame) frm <- eval.parent(mf) ## <- including "..." data[["(weights)"]] <- model.weights(frm) data[["(offset)"]] <- model.offset(frm) mform <- modelFormula(formula) isGLM <- !missing(family) ## TODO in future, consider isNLM / isNLS groups <- eval(mform$groups, frm) if (!is.factor(groups)) groups <- factor(groups) fit <- if (isGLM) glm else lm mf2 <- if (missing(family)) NULL else list(family=family) fitfun <- function(data, formula) { tryCatch({ do.call(fit, c(list(formula, data, ## don't use model.offset()/model.weights from stats() - warning with tibbles weights = data[["(weights)"]], offset = data[["(offset)"]], ...), mf2)) }, error = function(x) x) } ## split *original data*, not frm (derived model frame), on groups ## we have to do this because we need raw, not derived variables ## when evaluating linear regression. ## need to apply subset first ((or even much earlier ??)) ## (hope there aren't tricky interactions with NAs in subset ... ??) if (!missing(subset)) { data <- eval(substitute(data[subset,]), list2env(data)) } frm.split <- split(data, groups) ## NB: levels() is only OK if grouping variable is a factor nms <- names(frm.split) val <- ## mapply(fitfun, lapply(frm.split, fitfun, formula = as.formula(mform$model)) ## use warnErrList(), but expand msg for back compatibility and user-friendliness: val <- warnErrList(val, warn = FALSE) ## Contrary to nlme, we keep the erronous ones as well (with a warning): if(warn && length(wMsg <- attr(val,"warningMsg"))) { if(grepl("contrasts.* factors? .* 2 ", wMsg)){ # try to match translated msg, too warning("Fitting failed for ", sum(vapply(val, is.null, NA)), " group(s), probably because a factor only had one level", sub(".*:", ":\n ", wMsg), domain=NA) } else warning(wMsg, domain=NA) } new("lmList4", setNames(val, nms), call = mCall, pool = pool, groups = ordered(groups), origOrder = match(unique(as.character(groups)), nms) ) } ## (currently hidden) auxiliaries isGLMlist <- function(object, ...) { D <- getDataPart(object) length(D) >= 1 && inherits(D[[1]], "glm") } ## does a glm family have a "scale" [from stats:::logLik.glm() ] : .hasScale <- function(family) family %in% c("gaussian", "Gamma", "inverse.gaussian") family2char <- function(fam) { if(is.function(fam)) fam()$family else if(!is.character(fam)) fam$family else fam } ##' Does a lmList4 object have a "scale" / sigma / useScale ? hasScale <- function(object) !isGLMlist(object) || .hasScale(family(object[[1]])$family) ##' @importFrom stats coef ##' @S3method coef lmList4 ## Extract the coefficients and form a data.frame if possible ## FIXME: commented out nlme stuff (augFrame etc.). Restore, or delete for good ## FIXME: modified so that non-estimated values will be NA rather than set to ## coefs of first non-null estimate. Is that OK?? coef.lmList4 <- function(object, ## augFrame = FALSE, data = NULL, ##which = NULL, FUN = mean, omitGroupingFactor = TRUE, ...) { coefs <- lapply(object, coef) non.null <- !vapply(coefs, is.null, logical(1)) if (any(non.null)) { template <- coefs[non.null][[1]] ## different parameter sets may be estimated for different subsets of data ... allnames <- Reduce(union, lapply(coefs[non.null], names)) if (is.numeric(template)) { co <- matrix(NA, ncol = length(allnames), nrow = length(coefs), dimnames = list(names(object), allnames)) for (i in names(object)) { co[i,names(coefs[[i]])] <- coefs[[i]] } coefs <- as.data.frame(co) effectNames <- names(coefs) ## if(augFrame) { ## if (is.null(data)) { ## data <- getData(object) ## } ## data <- as.data.frame(data) ## if (is.null(which)) { ## which <- 1:ncol(data) ## } ## data <- data[, which, drop = FALSE] ## ## eliminating columns with same names as effects ## data <- data[, is.na(match(names(data), effectNames)), drop = FALSE] ## data <- gsummary(data, FUN = FUN, groups = getGroups(object)) ## if (omitGroupingFactor) { ## data <- data[, is.na(match(names(data), ## names(getGroupsFormula(object, ## asList = TRUE)))), ## drop = FALSE] ## } ## if (length(data) > 0) { ## coefs <- cbind(coefs, data[row.names(coefs),,drop = FALSE]) ## } ## } attr(coefs, "level") <- attr(object, "level") attr(coefs, "label") <- "Coefficients" attr(coefs, "effectNames") <- effectNames attr(coefs, "standardized") <- FALSE } ## is.numeric(template) } coefs } ### FIXME?: nlme *does* export this -- we export sigma() [instead ?] pooledSD <- function(x, allow.0.df = TRUE) { stopifnot(is(x, "lmList4")) if(!hasScale(x)) { if(allow.0.df) return(structure(1, df = NA)) ## scale := 1 if(!useScale) ## else stop("no scale, hence no pooled SD for this object") } sumsqr <- rowSums(sapply(x, function(el) { if (is.null(el)) { c(0,0) } else { res <- resid(el) c(sum(res^2), length(res) - length(coef(el))) } })) if (sumsqr[2] == 0) { ## FIXME? rather return NA with a warning ?? stop("No degrees of freedom for estimating std. dev.") } val <- sqrt(sumsqr[1]/sumsqr[2]) attr(val, "df") <- sumsqr[2] val } sigma.lmList4 <- function(object, ...) if(hasScale(object)) as.vector(pooledSD(object)) else 1 ## 1 for GLM <==> 1 when useScale is FALSE for [G]LMMs ##' @importFrom methods show ##' @exportMethod show setMethod("show", "lmList4", function(object) { mCall <- object@call cat("Call:", deparse(mCall), "\n") cat("Coefficients:\n") print(coef(object)) if (object@pool) { cat("\n") poolSD <- pooledSD(object) dfRes <- attr(poolSD, "df") RSE <- c(poolSD) cat("Degrees of freedom: ", length(unlist(lapply(object, fitted))), " total; ", dfRes, " residual\n", sep = "") cat("Residual standard error:", format(RSE)) cat("\n") } }) ##' @S3method confint lmList4 confint.lmList4 <- function(object, parm, level = 0.95, ...) { mCall <- match.call() if (length(object) < 1) return(new("lmList4.confint", array(numeric(0), c(0,0,0)))) mCall$object <- object[[1]] ## the old recursive strategy doesn't work with S3 objects -- ## calls "confint.lmList4" again instead of calling "confint" mCall[[1]] <- quote(confint) template <- eval(mCall) if(is.null(d <- dim(template))) ## MASS:::confint.profile.glm() uses drop(), giving vector d <- dim(template <- rbind("(Intercept)" = template)) template[] <- NA_real_ val <- array(template, c(d, length(object)), c(dimnames(template), list(names(object)))) pool <- list(...)$pool if (is.null(pool)) pool <- object$pool if (length(pool) > 0 && pool[1]) { ## do our own sd <- pooledSD(object) a <- (1 - level)/2 fac <- sd * qt(c(a, 1 - a), attr(sd, "df")) parm <- dimnames(template)[[1]] for (i in seq_along(object)) if(!is.null(ob.i <- object[[i]])) val[ , , i] <- coef(ob.i)[parm] + sqrt(diag(summary(object[[i]], corr = FALSE)$cov.unscaled )[parm]) %o% fac } else { ## build on confint() method for "glm" / "lm" : for (i in seq_along(object)) if(!is.null(mCall$object <- object[[i]])) { ci <- eval(mCall) if(is.null(dim(ci))) ## MASS:::confint.profile.glm() ... ci <- rbind("(Intercept)" = ci) if(identical(dim(ci), d)) val[ , , i] <- ci else ## some coefficients were not estimable val[rownames(ci), , i] <- ci } } new("lmList4.confint", aperm(val, 3:1)) } ##' @importFrom graphics plot ##' @importFrom lattice ....... ##' @S3method plot lmList4.confint plot.lmList4.confint <- function(x, y, order, ...) { ## stopifnot(require("lattice")) arr <- as(x, "array") dd <- dim(arr) dn <- dimnames(arr) levs <- dn[[1]] if (!missing(order) && (ord <- round(order[1])) %in% seq(dd[3])) levs <- levs[order(rowSums(arr[ , , ord]))] ll <- length(arr) df <- data.frame(group = ordered(rep(dn[[1]], dd[2] * dd[3]), levels = levs), intervals = as.vector(arr), what = gl(dd[3], dd[1] * dd[2], length = ll, labels = dn[[3]]), end = gl(dd[2], dd[1], length = ll)) panelfun <- function(x, y, pch = dot.symbol$pch, col = dot.symbol$col, cex = dot.symbol$cex, font = dot.symbol$font, ...) { x <- as.numeric(x) y <- as.numeric(y) ok <- !is.na(x) & !is.na(y) yy <- y[ok] xx <- x[ok] dot.symbol <- trellis.par.get("dot.symbol") dot.line <- trellis.par.get("dot.line") panel.abline(h = yy, lwd = dot.line$lwd, lty = dot.line$lty, col = dot.line$col) lpoints(xx, yy, pch = "|", col = col, cex = cex, font = font, ...) lower <- tapply(xx, yy, min) upper <- tapply(xx, yy, max) nams <- as.numeric(names(lower)) lsegments(lower, nams, upper, nams, col = col, lty = 1, lwd = if (dot.line$lwd) { dot.line$lwd } else { 2 }) } dotplot(group ~ intervals | what, data = df, scales = list(x="free"), panel=panelfun, ...) } ##' @importFrom stats update ##' @S3method update lmList4 update.lmList4 <- function(object, formula., ..., evaluate = TRUE) { call <- object@call if (is.null(call)) stop("need an object with call slot") extras <- match.call(expand.dots = FALSE)$... if (!missing(formula.)) call$formula <- update.formula(formula(object), formula.) if (length(extras) > 0) { existing <- !is.na(match(names(extras), names(call))) for (a in names(extras)[existing]) call[[a]] <- extras[[a]] if (any(!existing)) { call <- c(as.list(call), extras[!existing]) call <- as.call(call) } } if (evaluate) eval(call, parent.frame()) else call } ##' @importFrom stats formula ##' @S3method formula lmList4 ##' @return of class "formula" ==> as.formula() rather than just [["formula"]] formula.lmList4 <- function(x, ...) structure(x@call[["formula"]], class = "formula") ##' Get the grouping factor of an "lmList4" object ##' Important as auxiliary method for many of the nlme-imported methods: getGroups.lmList4 <- function(object, ...) object@groups ### All the other "lmList4" S3 methods are imported from nmle : ## .ns.nlme <- asNamespace("nlme") .ns.lme4 <- environment() ## == asNamespace("lme4") during build/load ## ## To do this, we need to make them use *our* namespace, e.g. to use our pooledSD() ## However, then we get from codetools : ## ## fitted.lmList4: no visible global function definition for 'getGroups' ## pairs.lmList4: no visible global function definition for 'gsummary' ## pairs.lmList4: no visible global function definition for 'getGroups' ## plot.lmList4: no visible global function definition for 'c_deparse' ## plot.lmList4: no visible global function definition for 'getGroups' ## predict.lmList4: no visible global function definition for 'getGroups' ## print.lmList4: no visible global function definition for 'c_deparse' ## qqnorm.lmList4: no visible global function definition for 'getGroups' ## qqnorm.lmList4: no visible global function definition for 'gsummary' ## residuals.lmList4: no visible global function definition for 'getGroups' ## ## which we avoid via for(fn in c("gsummary", "c_deparse")) { if (exists(fn, envir = .ns.nlme)) { assign(fn, get(fn, envir = .ns.nlme, inherits=FALSE)) } } for(fn in c("fitted", "fixef", "logLik", "pairs", "plot", "predict", ## "print", <- have our own show() "qqnorm", "ranef", "residuals", "summary")) { meth <- get(paste(fn, "lmList", sep="."), envir = .ns.nlme, inherits=FALSE) environment(meth) <- .ns.lme4 # e.g. in order to use *our* pooledSD() assign(paste(fn, "lmList4", sep="."), meth) } rm(fn) lme4/R/utilities.R0000644000176200001440000012536515113136610013463 0ustar liggesusers ## From Matrix package isDiagonal(.) : all0 <- function(x) !anyNA(x) && all(!x) .isDiagonal.sq.matrix <- function(M, n = dim(M)[1L]) all0(M[rep_len(c(FALSE, rep.int(TRUE,n)), n^2)]) ### Utilities for parsing and manipulating mixed-model formulas ## abbreviated parse for long strings: deparse1() pastes w/ collapse instead abbrDeparse <- function(x, width=60) { r <- deparse(x, width) if(length(r) > 1) paste(r[1], "...") else r } ##' @param bars result of findbars barnames <- function(bars) vapply(bars, function(x) deparse1(x[[3]]), "") makeFac <- function(x,char.only=FALSE) { if (!is.factor(x) && (!char.only || is.character(x))) factor(x) else x } factorize <- function(x,frloc,char.only=FALSE) { ## convert grouping variables to factors as necessary ## TODO: variables that are *not* in the data frame are ## not converted -- these could still break, e.g. if someone ## tries to use the : operator ## TODO: some sensible tests for drop.unused.levels ## (not actually used, but could come in handy) for (i in all.vars(RHSForm(x))) { if (!is.null(curf <- frloc[[i]])) frloc[[i]] <- makeFac(curf,char.only) } return(frloc) } colSort <- function(x) { termlev <- vapply(strsplit(x,":"),length,integer(1)) iterms <- split(x,termlev) iterms <- sapply(iterms,sort,simplify=FALSE) ## make sure intercept term is first ilab <- "(Intercept)" if (ilab %in% iterms[[1]]) { iterms[[1]] <- c(ilab,setdiff(iterms[[1]],ilab)) } unlist(iterms) } ## copied from glmmTMB, replace by upstream utility package? ## test formula: does it contain a particular element? ## inForm(z~.,quote(.)) ## inForm(z~y,quote(.)) ## inForm(z~a+b+c,quote(c)) ## inForm(z~a+b+(d+e),quote(c)) ## f <- ~ a + offset(x) ## f2 <- z ~ a ## inForm(f,quote(offset)) ## inForm(f2,quote(offset)) ## @export ## @keywords internal inForm <- function(form,value) { if (any(sapply(form,identical,value))) return(TRUE) if (all(sapply(form,length)==1)) return(FALSE) return(any(vapply(form,inForm,value,FUN.VALUE=logical(1)))) } ## was called "replaceForm" there but replaceTerm is better ## (decide on camelCase vs snake_case!) replaceTerm <- function(term,target,repl) { if (identical(term,target)) return(repl) if (!inForm(term,target)) return(term) if (length(term) == 2) { return(substitute(OP(x),list(OP=replaceTerm(term[[1]],target,repl), x=replaceTerm(term[[2]],target,repl)))) } return(substitute(OP(x,y),list(OP=replaceTerm(term[[1]],target,repl), x=replaceTerm(term[[2]],target,repl), y=replaceTerm(term[[3]],target,repl)))) } `%i%` <- function(f1, f2, fix.order = TRUE) { if (!is.factor(f1) || !is.factor(f2)) stop("both inputs must be factors") f12 <- paste(f1, f2, sep = ":") ## explicitly specifying levels is faster in any case ... u <- which(!duplicated(f12)) if (!fix.order) return(factor(f12, levels = f12[u])) ## deal with order of factor levels levs_rank <- length(levels(f2))*as.numeric(f1[u])+as.numeric(f2[u]) return(factor(f12, levels = (f12[u])[order(levs_rank)])) } ##' @param x a language object of the form effect | groupvar ##' @param frloc model frame ##' @param drop.unused.levels (logical) ##' @return list containing grouping factor, sparse model matrix, number of levels, names mkBlist <- function(x,frloc, drop.unused.levels=TRUE, reorder.vars=FALSE) { frloc <- factorize(x,frloc) ## try to evaluate grouping factor within model frame ... ff0 <- replaceTerm(x[[3]], quote(`:`), quote(`%i%`)) ff <- try(eval(substitute(makeFac(fac), list(fac = ff0)), frloc), silent = TRUE) if (inherits(ff, "try-error")) { stop("couldn't evaluate grouping factor ", deparse1(x[[3]])," within model frame:", "error =", c(ff), " Try adding grouping factor to data ", "frame explicitly if possible",call.=FALSE) } if (all(is.na(ff))) stop("Invalid grouping factor specification, ", deparse1(x[[3]]),call.=FALSE) ## NB: *also* silently drops levels - and mkReTrms() and hence ## predict.merMod() have relied on that property : if (drop.unused.levels) ff <- factor(ff, exclude=NA) nl <- length(levels(ff)) ## this section implements eq. 6 of the JSS lmer paper ## model matrix based on LHS of random effect term (X_i) ## x[[2]] is the LHS (terms) of the a|b formula has.sparse.contrasts <- function(x) { cc <- attr(x, "contrasts") !is.null(cc) && is(cc, "sparseMatrix") } any.sparse.contrasts <- any(vapply(frloc, has.sparse.contrasts, FUN.VALUE = TRUE)) mMatrix <- if (!any.sparse.contrasts) model.matrix else sparse.model.matrix mm <- mMatrix(eval(substitute( ~ foo, list(foo = x[[2]]))), frloc) if (reorder.vars) { mm <- mm[colSort(colnames(mm)),] } ## this is J^T (see p. 9 of JSS lmer paper) ## construct indicator matrix for groups by observations ## use fac2sparse() rather than as() to allow *not* dropping ## unused levels where desired sm <- fac2sparse(ff, to = "d", drop.unused.levels = drop.unused.levels) sm <- KhatriRao(sm, t(mm)) dimnames(sm) <- list( rep(levels(ff),each=ncol(mm)), rownames(mm)) list(ff = ff, sm = sm, nl = nl, cnms = colnames(mm)) } ##' Create an lmerResp, glmResp or nlsResp instance ##' ##' @title Create an lmerResp, glmResp or nlsResp instance ##' @param fr a model frame ##' @param REML logical scalar, value of REML for an lmerResp instance ##' @param family the optional glm family (glmResp only) ##' @param nlenv the nonlinear model evaluation environment (nlsResp only) ##' @param nlmod the nonlinear model function (nlsResp only) ##' @param ... where to look for response information if \code{fr} is missing. ##' Can contain a model response, \code{y}, offset, \code{offset}, and weights, ##' \code{weights}. ##' @return an lmerResp or glmResp or nlsResp instance ##' @family utilities ##' @export mkRespMod <- function(fr, REML=NULL, family = NULL, nlenv = NULL, nlmod = NULL, ...) { if(!missing(fr)) { y <- model.response(fr) offset <- model.offset(fr) weights <- model.weights(fr) N <- n <- nrow(fr) etastart_update <- model.extract(fr, "etastart") mustart_update <- model.extract(fr, "mustart") } else { fr <- list(...) y <- fr$y N <- n <- NROW(y) offset <- fr$offset weights <- fr$weights etastart_update <- fr$etastart mustart_update <- fr$mustart } if(length(dim(y)) == 1L) y <- drop(y) ## avoid problems with 1D arrays and keep names if(isGLMM <- !is.null(family)) stopifnot(inherits(family, "family")) ## FIXME: may need to add X, or pass it somehow, if we want to use glm.fit ## test for non-numeric response here to avoid later ## confusing error messages from deeper machinery if (!is.null(y)) { ## 'y' may be NULL if we're doing simulation if(!(is.numeric(y) || ((is.binom <- isGLMM && family$family == "binomial") && (is.factor(y) || is.logical(y))))) { if (is.binom) stop("response must be numeric or factor") else { if (is.logical(y)) y <- as.integer(y) else stop("response must be numeric") } } if(!all(is.finite(y))) stop("NA/NaN/Inf in 'y'") # same msg as from lm.fit() } rho <- new.env() rho$y <- if (is.null(y)) numeric(0) else y if (!is.null(REML)) rho$REML <- REML rho$etastart <- etastart_update rho$mustart <- mustart_update rho$start <- attr(fr,"start") if (!is.null(nlenv)) { stopifnot(is.language(nlmod), is.environment(nlenv), is.numeric(val <- eval(nlmod, nlenv)), length(val) == n, ## FIXME? Restriction, not present in ole' nlme(): is.matrix(gr <- attr(val, "gradient")), is.numeric(gr), nrow(gr) == n, !is.null(pnames <- colnames(gr))) N <- length(gr) rho$mu <- as.vector(val) rho$sqrtXwt <- as.vector(gr) rho$gam <- ## FIXME more efficient mget(pnames, envir=nlenv) unname(unlist(lapply(pnames, function(nm) get(nm, envir=nlenv)))) } rho$offset <- if (!is.null(offset)) { if (length(offset) == 1L) offset <- rep.int(offset, N) else stopifnot(length(offset) == N) unname(offset) } else rep.int(0, N) rho$weights <- if (!is.null(weights)) { stopifnot(length(weights) == n, all(weights >= 0)) unname(weights) } else rep.int(1, n) if(isGLMM) { ## need weights for initializing evaluation rho$nobs <- n ## allow trivial objects, e.g. for simulation if (length(y)>0) eval(family$initialize, rho) ## ugh. this *is* necessary; ## family$initialize *ignores* mustart in env, overwrites! ## see ll 180-182 of src/library/stats/R/glm.R ## https://github.com/wch/r-source/search?utf8=%E2%9C%93&q=mukeep if (!is.null(mustart_update)) rho$mustart <- mustart_update ## family$initialize <- NULL # remove clutter from str output ll <- as.list(rho) ans <- do.call(new, c(list(Class="glmResp", family=family), ll[setdiff(names(ll), c("m", "nobs", "mustart"))])) if (length(y)>0) ans$updateMu(if (!is.null(es <- etastart_update)) es else family$linkfun(rho$mustart)) ans } else if (is.null(nlenv)) ## lmer do.call(lmerResp$new, as.list(rho)) else ## nlmer do.call(nlsResp$new, c(list(nlenv=nlenv, nlmod=substitute(~foo, list(foo=nlmod)), pnames=pnames), as.list(rho))) } subnms <- function(form, nms) { ## Recursive function applied to individual terms sbnm <- function(term) { if (is.name(term)) { if (any(term == nms)) 0 else term } else switch(length(term), term, ## 1 { ## 2 term[[2]] <- sbnm(term[[2]]) term }, { ## 3 term[[2]] <- sbnm(term[[2]]) term[[3]] <- sbnm(term[[3]]) term }) } sbnm(form) } ##' Check for a constant term (a literal 1) in an expression ## ##' In the mixed-effects part of a nonlinear model formula, a constant ##' term is not meaningful because every term must be relative to a ##' nonlinear model parameter. This function recursively checks the ##' expressions in the formula for a a constant, calling stop() if ##' such a term is encountered. ##' @title Check for constant terms. ##' @param expr an expression ##' @return NULL. The function is executed for its side effect. chck1 <- function(expr) { if ((le <- length(expr)) == 1) { if (is.numeric(expr) && expr == 1) stop("1 is not meaningful in a nonlinear model formula") return() } else for (j in seq_len(le)[-1]) Recall(expr[[j]]) } ## ---> ../man/nlformula.Rd --- Manipulate a nonlinear model formula ##' @param mc matched call from the caller, with arguments 'formula','start',... ##' @return a list with components "respMod", "frame", "X", "reTrms" nlformula <- function(mc) { start <- eval(mc$start, parent.frame(2L)) if (is.numeric(start)) start <- list(nlpars = start) stopifnot(is.numeric(nlpars <- start$nlpars), lengths(nlpars) == 1L, length(pnames <- names(nlpars)) == length(nlpars), length(form <- as.formula(mc$formula)) == 3L, is(nlform <- eval(form[[2]]), "formula"), pnames %in% all.vars(nlmod <- as.call(nlform[[lnl <- length(nlform)]]))) ## MM{FIXME}: fortune(106) even twice in here! nlform[[lnl]] <- parse(text= paste(setdiff(all.vars(form), pnames), collapse=' + '))[[1]] nlform <- eval(nlform) environment(nlform) <- environment(form) m <- match(c("data", "subset", "weights", "na.action", "offset"), names(mc), 0) mc <- mc[c(1, m)] mc$drop.unused.levels <- TRUE mc[[1L]] <- quote(stats::model.frame) mc$formula <- nlform fr <- eval(mc, parent.frame(2L)) n <- nrow(fr) nlenv <- list2env(fr, parent=parent.frame(2L)) lapply(pnames, function(nm) nlenv[[nm]] <- rep.int(nlpars[[nm]], n)) respMod <- mkRespMod(fr, nlenv=nlenv, nlmod=nlmod) chck1(meform <- form[[3L]]) pnameexpr <- parse(text=paste(pnames, collapse='+'))[[1]] nb <- nobars_(meform) ## call ORIGINAL recursive form fe <- eval(substitute(~ 0 + nb + pnameexpr)) environment(fe) <- environment(form) frE <- do.call(rbind, lapply(seq_along(nlpars), function(i) fr)) # rbind s copies of the frame for (nm in pnames) # convert these variables in fr to indicators frE[[nm]] <- as.numeric(rep(nm == pnames, each = n)) X <- model.matrix(fe, frE) rownames(X) <- NULL reTrms <- reformulas::mkReTrms(lapply(reformulas::findbars(meform), function(expr) { expr[[2]] <- substitute(0+foo, list(foo=expr[[2]])) expr }), frE) list(respMod=respMod, frame=fr, X=X, reTrms=reTrms, pnames=pnames) } ## {nlformula} ################################################################################ ## Beginning to think about exposing tools to create devcomp lists. ## Could be useful when extending merMod objects. Commenting them out ## however, because R CMD check is complaining: ## https://github.com/lme4/lme4/commit/8d71e439758999ea8f90eb4752487e189407ef33#commitcomment-8773017 ################################################################################ ## ## .dims <- function(pp, resp, nAGQ, ## reTrms, n, p, rcl, ## compDev = NULL) { ## if(missing(rcl)) rcl <- class(resp) ## if(missing(n)) n <- nrow(pp$V) ## if(missing(p)) p <- ncol(pp$V) ## c(N=nrow(pp$X), n=n, p=p, nmp=n-p, ## nth=length(pp$theta), q=nrow(pp$Zt), ## nAGQ=rho$nAGQ, ## compDev=rho$compDev, ## ## 'use scale' in the sense of whether dispersion parameter should ## ## be reported/used (*not* whether theta should be scaled by sigma) ## useSc=(rcl != "glmResp" || ## !resp$family$family %in% c("poisson","binomial")), ## reTrms=length(reTrms$cnms), ## spFe=0L, ## REML=if (rcl=="lmerResp") resp$REML else 0L, ## GLMM=(rcl=="glmResp"), ## NLMM=(rcl=="nlsResp")) ## } ## ## .cmp <- function(pp, resp, dims, fval, ## wrss, sqrLenU, pwrss, ## sigmaML, rcl, fac, ## tolPwrss = NULL, ## trivial.y = FALSE) { ## if(missing(rcl)) rcl <- class(resp) ## if(missing(fac)) fac <- as.numeric(rcl != "nlsResp") ## if(missing(wrss)) wrss <- resp$wrss() ## if(missing(sqrLenU)) sqrLenU <- pp$sqrL(fac) ## if(missing(pwrss)) pwrss <- wrss + sqrLenU ## if(missing(sigmaML)) sigmaML <- pwrss/dims['n'] ## c(ldL2=pp$ldL2(), ldRX2=pp$ldRX2(), wrss=wrss, ## ussq=sqrLenU, pwrss=pwrss, ## drsum=if (rcl=="glmResp" && !trivial.y) resp$resDev() else NA, ## REML=if (rcl=="lmerResp" && resp$REML != 0L && !trivial.y) ## opt$fval else NA, ## ## FIXME: construct 'REML deviance' here? ## dev=if (rcl=="lmerResp" && resp$REML != 0L || trivial.y) NA else opt$fval, ## sigmaML=sqrt(unname(if (!dims["useSc"] || trivial.y) NA else sigmaML)), ## sigmaREML=sqrt(unname(if (rcl!="lmerResp" || trivial.y) NA else sigmaML*(dims['n']/dims['nmp']))), ## tolPwrss=rho$tolPwrss) ## } ################################################################################ .minimalOptinfo <- function() list(conv = list(opt = 0L, lme4 = list(messages = character(0)))) getConv <- function(x) { if (!is.null(x[["conv"]])) { x[["conv"]] } else x[["convergence"]] } getMsg <- function(x) { if (!is.null(x[["msg"]])) { x[["msg"]] } else if (!is.null(x[["message"]])) { x[["message"]] } else "" } .optinfo <- function(opt, lme4conv=NULL) list(optimizer = attr(opt, "optimizer"), control = attr(opt, "control"), derivs = attr(opt, "derivs"), conv = list(opt = getConv(opt), lme4 = lme4conv), feval = if (is.null(opt$feval)) NA else opt$feval, message = getMsg(opt), warnings = attr(opt, "warnings"), val = opt$par) ##' Potentially needed in more than one place, be sure to keep consistency! ##' hack (NB families have weird names) from @aosmith16; then corrected isNBfamily <- function(familyString) grepl("^Negative ?Binomial", familyString, ignore.case=TRUE) normalizeFamilyName <- function(family) { # such as object@resp$family if(isNBfamily(family$family)) family$family <- "negative.binomial" family } ##' Is it a family with no scale parameter hasNoScale <- function(family) any(substr(family$family, 1L, 12L) == c("poisson", "binomial", "negative.bin", "Negative Bin")) ##--> ../man/mkMerMod.Rd ---Create a merMod object ##' @param rho the environment of the objective function ##' @param opt the value returned by the optimizer ##' @param reTrms reTrms list from the calling function mkMerMod <- function(rho, opt, reTrms, fr, mc, lme4conv=NULL) { if(missing(mc)) mc <- match.call() stopifnot(is.environment(rho), is(pp <- rho$pp, "merPredD"), is(resp <- rho$resp, "lmResp"), is.list(opt), "par" %in% names(opt), c("conv", "fval") %in% substr(names(opt),1,4), ## "conv[ergence]", "fval[ues]" is.list(reTrms), c("flist", "cnms", "Gp", "lower") %in% names(reTrms), length(rcl <- class(resp)) == 1) n <- nrow(pp$V) p <- ncol(pp$V) isGLMM <- (rcl == "glmResp") dims <- c(N = nrow(pp$X), n=n, p=p, nmp = n-p, q = nrow(pp$Zt), nth = length(pp$theta), nAGQ= rho$nAGQ, compDev=rho$compDev, ## 'use scale' in the sense of whether dispersion parameter should ## be reported/used (*not* whether theta should be scaled by sigma) useSc = !(isGLMM && hasNoScale(resp$family)), reTrms=length(reTrms$cnms), spFe= 0L, REML = if (rcl=="lmerResp") resp$REML else 0L, GLMM= isGLMM, NLMM= (rcl=="nlsResp")) storage.mode(dims) <- "integer" fac <- as.numeric(rcl != "nlsResp") if (trivial.y <- (length(resp$y)==0)) { ## trivial model sqrLenU <- wrss <- pwrss <- NA } else { sqrLenU <- pp$sqrL(fac) wrss <- resp$wrss() pwrss <- wrss + sqrLenU } ## weights <- resp$weights beta <- pp$beta(fac) sigmaML <- pwrss/n if (rcl != "lmerResp") { pars <- opt$par if (length(pars) > length(pp$theta)) beta <- pars[-(seq_along(pp$theta))] } cmp <- c(ldL2=pp$ldL2(), ldRX2=pp$ldRX2(), wrss=wrss, ussq=sqrLenU, pwrss=pwrss, drsum=if (rcl=="glmResp" && !trivial.y) resp$resDev() else NA, REML=if (rcl=="lmerResp" && resp$REML != 0L && !trivial.y) opt$fval else NA, ## FIXME: construct 'REML deviance' here? dev=if (rcl=="lmerResp" && resp$REML != 0L || trivial.y) NA else opt$fval, sigmaML=sqrt(unname(if (!dims["useSc"] || trivial.y) NA else sigmaML)), sigmaREML=sqrt(unname(if (rcl!="lmerResp" || trivial.y) NA else sigmaML*(dims['n']/dims['nmp']))), tolPwrss=rho$tolPwrss) ## TODO: improve this hack to get something in frame slot (maybe need weights, etc...) if(missing(fr)) fr <- data.frame(resp$y) new(switch(rcl, lmerResp = "lmerMod", glmResp = "glmerMod", nlsResp = "nlmerMod"), call=mc, frame=fr, flist=reTrms$flist, cnms=reTrms$cnms, Gp=reTrms$Gp, theta=pp$theta, beta=beta, u=if (trivial.y) rep(NA_real_,nrow(pp$Zt)) else pp$u(fac), lower=reTrms$lower, devcomp=list(cmp=cmp, dims=dims), pp=pp, resp=resp, optinfo = .optinfo(opt, lme4conv)) }## {mkMerMod} ## generic argument checking ## 'type': name of calling function ("glmer", "lmer", "nlmer") ## ## NB: called from lFormula() and glFormula() checkArgs <- function(type,...) { l... <- list(...) if (isTRUE(l...[["sparseX"]])) warning("sparseX = TRUE has no effect at present",call.=FALSE) ## '...' handling up front, safe-guarding against typos ("familiy") : if(length(l... <- list(...))) { if (!is.null(l...[["family"]])) { # call glmer if family specified ## we will only get here if 'family' is *not* in the arg list warning("calling lmer with family() is deprecated: please use glmer() instead",call.=FALSE) type <- "glmer" } ## Check for method argument which is no longer used ## (different meanings/hints depending on glmer vs lmer) if (!is.null(l...[["method"]])) { msg <- paste("Argument", sQuote("method"), "is deprecated.") if (type == "lmer") msg <- paste(msg, "Use the REML argument to specify ML or REML estimation.") else if (type == "glmer") msg <- paste(msg, "Use the nAGQ argument to specify Laplace (nAGQ=1) or adaptive", "Gauss-Hermite quadrature (nAGQ>1). PQL is no longer available.") warning(msg,call.=FALSE) l... <- l...[names(l...) != "method"] } if(length(l...)) { warning("extra argument(s) ", paste(sQuote(names(l...)), collapse=", "), " disregarded",call.=FALSE) } } } ## check formula and data: return an environment suitable for evaluating ## the formula. ## (1) if data is specified, return it ## (2) otherwise, if formula has an environment, use it ## (3) otherwise [e.g. if formula was passed as a string], try to use parent.frame(2) ## if #3 is true *and* the user is doing something tricky with nested functions, ## this may fail ... ## try to diagnose missing/bad data checkFormulaData <- function(formula, data, checkLHS=TRUE, checkData=TRUE, debug=FALSE) { wd <- tryCatch(force(data), error = identity) if (bad.data <- inherits(wd,"error")) { bad.data.msg <- wd$message } ## data not found (this *should* only happen with garbage input, ## OR when strings used as formulae -> drop1/update/etc.) ## if (bad.data || debug) { varex <- function(v, env) exists(v, envir=env, inherits=FALSE) allvars <- all.vars(as.formula(formula)) allvarex <- function(env, vvec=allvars) all(vapply(vvec, varex, NA, env)) } if (bad.data) { ## Choose helpful error message: if (allvarex(environment(formula))) { stop("bad 'data', but variables found in environment of formula: ", "try specifying 'formula' as a formula rather ", "than a string in the original model",call.=FALSE) } else { stop("bad 'data': ", bad.data.msg, call. = FALSE) } } else { denv <- ## The data as environment if (is.null(data)) { if (!is.null(ee <- environment(formula))) { ee ## use environment of formula } else { ## e.g. no environment, e.g. because formula is a character vector ## parent.frame(2L) works because [g]lFormula (our calling environment) ## has been called within [g]lmer with env=parent.frame(1L) ## If you call checkFormulaData in some other bizarre way such that ## parent.frame(2L) is *not* OK, you deserve what you get ## calling checkFormulaData directly from the global ## environment should be OK, since trying to go up beyond the global ## environment keeps bringing you back to the global environment ... parent.frame(2L) } } else ## data specified list2env(data) } ## ## FIXME: set enclosing environment of denv to environment(formula), or parent.frame(2L) ? if (debug) { cat("Debugging parent frames in checkFormulaData:\n") ## find global environment -- could do this with sys.nframe() ? glEnv <- 1L while (!identical(parent.frame(glEnv),.GlobalEnv)) { glEnv <- glEnv+1L } ## where are vars? for (i in 1:glEnv) { OK <- allvarex(parent.frame(i)) cat("vars exist in parent frame ", i) if (i == glEnv) cat(" (global)") cat(" ",OK, "\n") } cat("vars exist in env of formula ", allvarex(denv), "\n") } ## if (debug) stopifnot(!checkLHS || length(as.formula(formula,env=denv)) == 3) ## check for two-sided formula return(denv) } ## checkFormulaData <- function(formula,data) { ## ee <- environment(formula) ## if (is.null(ee)) { ## ee <- parent.frame(2) ## } ## if (missing(data)) data <- ee ## stopifnot(length(as.formula(formula,env=as.environment(data))) == 3) ## return(data) ## } ##' Not exported; for tests (and examples) that can be slow; ##' Use if(lme4:::testLevel() >= 1.) ..... see ../tests/README.md testLevel <- function() if(nzchar(s <- Sys.getenv("LME4_TEST_LEVEL")) && is.finite(s <- as.numeric(s))) s else 1 ##' General conditional variance-covariance matrix ##' ##' Experimental function for estimating the variance-covariance ##' matrix of the random effects, conditional on the observed data ##' and at the (RE)ML estimate of the fixed effects and covariance ##' parameters. Applicable for any Lambda matrix, but slower than ##' other block-by-block methods. ##' Not exported. ##' ##' TODO: ##' - Write up quick note on theory (e.g. Laplace approximation). ##' - Test. Speed? Correctness? ##' - Do we need to think carefully about the differences ##' between REML and ML, beyond just multiplying by a different ##' sigma^2 estimate? ##' - is it better to do this term-by-term as in C++ code? ##' ##' @param object \code{merMod} object ##' @return Sparse covariance matrix condVar <- function(object, scaled=TRUE) { Lamt <- getME(object, "Lambdat") L <- getME(object, "L") ## never do it this way! fortune("SOOOO") #V <- solve(L, system = "A") #V <- chol2inv(L) #s2*crossprod(Lamt, V) %*% Lamt LL <- solve(L, Lamt, system = "A") ## From ?Matrix::solve, The default, '"A"', is to solve Ax = b for x ## where 'A' is sparse, positive-definite matrix that was ## factored to produce 'a'. cc <- crossprod(Lamt, LL) if (scaled) cc <- sigma(object)^2*cc cc } mkMinimalData <- function(formula) { vars <- all.vars(formula) nVars <- length(vars) matr <- matrix(0, 2, nVars) data <- as.data.frame(matr) setNames(data, vars) } ##' Make template for mixed model parameters mkParsTemplate <- function(formula, data){ if(missing(data)) data <- mkMinimalData(formula) mfRanef <- model.frame( reformulas::subbars(formula), data) mmFixef <- model.matrix( reformulas::nobars(formula) , data) reTrms <- reformulas::mkReTrms( reformulas::findbars(formula), mfRanef) cnms <- reTrms$cnms thetaNamesList <- mapply(mkPfun(), names(cnms), cnms) thetaNames <- unlist(thetaNamesList) betaNames <- colnames(mmFixef) list(beta = setNames(numeric(length( betaNames)), betaNames), theta = setNames(reTrms$theta, thetaNames), sigma = 1) } ##' Make template for mixed model data ##' ##' Useful for simulating balanced designs and for ##' getting started on unbalanced simulations ##' ##' @param formula formula ##' @param data data -- not necessary ##' @param nGrps number of groups per grouping factor ##' @param rfunc function for generating covariate data ##' @param ... additional parameters for rfunc mkDataTemplate <- function(formula, data, nGrps = 2, nPerGrp = 1, rfunc = NULL, ...){ if(missing(data)) data <- mkMinimalData(formula) grpFacNames <- unique(barnames(reformulas::findbars(formula))) varNames <- all.vars(formula) covariateNames <- setdiff(varNames, grpFacNames) nGrpFac <- length(grpFacNames) nCov <- length(covariateNames) grpFac <- gl(nGrps, nPerGrp) grpDat <- expand.grid(replicate(nGrpFac, grpFac, simplify = FALSE)) colnames(grpDat) <- grpFacNames nObs <- nrow(grpDat) if(is.null(rfunc)) rfunc <- function(n, ...) rep(0, n) params <- c(list(nObs), list(...)) covDat <- as.data.frame(replicate(nCov, do.call(rfunc, params), simplify = FALSE)) colnames(covDat) <- covariateNames cbind(grpDat, covDat) } ##' very flexible and convenient wrt formula, ##' very unflexible wrt everything else ##' ##' starting to get a little too sugary? quickSimulate <- function(formula, nGrps, nPerGrp, family = gaussian) { pr <- mkParsTemplate(formula) dt <- mkDataTemplate(formula, nGrps = nGrps, nPerGrp = nPerGrp, rfunc = rnorm) response <- deparse(formula[[2]]) dt[[response]] <- simulate(formula, newdata = dt, newparams = pr, family = family)[[1]] return(dt) } #---------------------------------------------------------------------- # formula parsing sugar #---------------------------------------------------------------------- ##' these functions pick up where findbars leaves off, in terms of sugar ##' @param REtrm an element of the result of findbars ##' @param REtrms the result of findbars ##' @return \code{reexpr} gives a one-sided formula with the linear ##' model formula for the raw model matrix. \code{grpfact} gives an ##' expression with the name of the grouping factor associated with ##' the raw model matrix. \code{termnms} gives a character vector with ##' the names of the random effects terms. reexpr <- function(REtrm) substitute( ~ foo, list(foo = REtrm[[2]])) grpfact <- function(REtrm) substitute(factor(fac), list(fac = REtrm[[3]])) termnms <- function(REtrms) vapply(REtrms, deparse1, "") ##' mmList(): list of model matrices ##' ------ called from getME() & model.matrix(*, "randomListRaw") mmList <- function(object, ...) UseMethod("mmList") mmList.merMod <- function(object, ...) mmList(formula(object), model.frame(object)) mmList.formula <- function(object, frame, ...) { bars <- reformulas::findbars(object) mm <- setNames(lapply(bars, function(b) model.matrix(eval(reexpr(b), frame), frame)), termnms(bars)) grp <- lapply(lapply(bars, grpfact), eval, frame) nl <- vapply(grp, nlevels, 1L) if (any(diff(nl) > 0)) mm[order(nl, decreasing=TRUE)] else mm } ##' examples ---FIXME?--- put in tests // or export + 'real examples' if(FALSE) { library(lme4) m <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) gm <- glmer(cbind(incidence, size-incidence) ~ period + (1|herd), cbpp, binomial) simForm <- y ~ x + z + (x | f) + (z | g) ## ::: triggers R CMD check NOTE ## simDat <- lme4:::quickSimulate(simForm, 10, 5) simDat <- simDat[simDat$f != "10", ] # unbalancedish design requiring # a flip in the order of terms sm <- lmer(simForm, simDat) ## ::: triggers R CMD check NOTE ## lme4:::mmList.merMod(m) ## lme4:::mmList.merMod(gm) ## smmm <- lme4:::mmList.merMod(sm) } nloptwrap <- local({ ## define default control values in environment of function ... defaultControl <- list(algorithm="NLOPT_LN_BOBYQA", xtol_abs=1e-8, ftol_abs=1e-8, maxeval=1e5) ## function(par, fn, lower, upper, control=list(),...) { for (n in names(defaultControl)) if (is.null(control[[n]])) control[[n]] <- defaultControl[[n]] res <- nloptr(x0=par, eval_f=fn, lb=lower, ub=upper, opts=control, ...) with(res, list(par = solution, fval = objective, feval = iterations, ## ?nloptr: "integer value with the status of the optimization (0 is success)" ## most status>0 are fine (e.g. 4 "stopped because xtol_rel was reached" ## but status 5 is "ran out of evaluations" conv = if (status<0 || status==5) status else 0, message = message)) } }) nlminbwrap <- function(par, fn, lower, upper, control=list(), ...) { if (!is.null(control$maxfun)) { control$eval.max <- control$maxfun control$maxfun <- NULL } res <- nlminb(start = par, fn, gradient = NULL, hessian = NULL, scale = 1, lower = lower, upper = upper, control = control, ...) list(par = res$par, fval = res$objective, conv = res$convergence, message = res$message) } glmerLaplaceHandle <- function(pp, resp, nAGQ, tol, maxit, verbose) { .Call(glmerLaplace, pp, resp, nAGQ, tol, as.integer(maxit), verbose) } isFlexLambda <- function() FALSE #' convert a list of matrices (n, pxp blocks) to a p x p x n array mlist_to_array <- function(m) { p <- nrow(m[[1]]) n <- length(m) array(unlist(lapply(m,as.matrix)),dim=c(p,p,n)) } #' @inheritParams bdiag_to_array bdiag_to_mlist <- function(m,n) { if (length(n)==1 && n1) parallel <- match.arg(parallel) do_parallel <- (parallel != "no" && ncpus > 1L) if (do_parallel) { if (parallel == "multicore") have_mc <- .Platform$OS.type != "windows" else if (parallel == "snow") have_snow <- TRUE if (!(have_mc || have_snow)) do_parallel <- FALSE # (only for "windows") } }) getSingTol <- function() { getOption("lme4.singular.tolerance", 1e-4) } isSingular <- function(x, tol = getSingTol()) { lwr <- getME(x, "lower") theta <- getME(x, "theta") any(theta[lwr==0] < tol) } lme4_testlevel <- function() if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 # stolen from car package # the following unexported function is useful for combining results of parallel computations combineLists <- function(..., fmatrix="list", flist="c", fvector="rbind", fdf="rbind", recurse=FALSE){ # combine lists of the same structure elementwise # ...: a list of lists, or several lists, each of the same structure # fmatrix: name of function to apply to matrix elements # flist: name of function to apply to list elements # fvector: name of function to apply to data frame elements # recurse: process list element recursively frecurse <- function(...){ combineLists(..., fmatrix=fmatrix, fvector=fvector, fdf=fdf, recurse=TRUE) } if (recurse) flist="frecurse" list.of.lists <- list(...) if (length(list.of.lists) == 1){ list.of.lists <- list.of.lists[[1]] list.of.lists[c("fmatrix", "flist", "fvector", "fdf")] <- c(fmatrix, flist, fvector, fdf) return(do.call("combineLists", list.of.lists)) } if (any(!sapply(list.of.lists, is.list))) stop("arguments are not all lists") len <- sapply(list.of.lists, length) if (any(len[1] != len)) stop("lists are not all of the same length") nms <- lapply(list.of.lists, names) if (any(unlist(lapply(nms, "!=", nms[[1]])))) stop("lists do not all have elements of the same names") nms <- nms[[1]] result <- vector(len[1], mode="list") names(result) <- nms for(element in nms){ element.list <- lapply(list.of.lists, "[[", element) # clss <- sapply(element.list, class) clss <- lapply(element.list, class) # if (any(clss[1] != clss)) stop("list elements named '", element, if (!all(vapply(clss, function(e) all(e == clss[[1L]]), NA))) stop("list elements named '", element, "' are not all of the same class") is.df <- is.data.frame(element.list[[1]]) fn <- if (is.matrix(element.list[[1]])) fmatrix else if (is.list(element.list[[1]]) && !is.df) flist else if (is.vector(element.list[[1]])) fvector else if (is.df) fdf else stop("list elements named '", element, "' are not matrices, lists, vectors, or data frames") result[[element]] <- do.call(fn, element.list) } result } ## copied from glmmTMB::check_dots checkDots <- function (..., .ignore = NULL, .action = "stop") { L <- list(...) if (length(.ignore) > 0) { L <- L[!names(L) %in% .ignore] } if (length(L) > 0) { FUN <- get(.action) FUN("unknown arguments: ", paste(names(L), collapse = ",")) } return(NULL) } ## quadratic form from emulator package: ## quad.tform == x %*% M %*% t(x) ## quad.tdiag == diag(quad.tform(M, x) ## rowSums(tcrossprod(Conj(x), M) * x) quad.tdiag <- function(M, x) { ## only real-valued, so drop Conj rowSums(tcrossprod(x, M) * x) } ##' attempt to modularize vcov scaling; more details in the autoscale vignette ##' @param vv represents the variance-covariance matrix before modification ##' @param sc represents the scale vector ##' @param ce represents the center vector scale_vcov <- function(vv, sc, ce) { other_vars <- setdiff(colnames(vv), "(Intercept)") ## 1. Modifying the intercept sig_0sq <- vv["(Intercept)", "(Intercept)"] sig_0isq <- vv["(Intercept)", other_vars] total1 <- -2 *sum((ce/sc) * sig_0isq) small_vv <- as.matrix(vv[other_vars, other_vars]) total2 <- crossprod(ce / sc, small_vv %*% (ce / sc))[[1]] vv["(Intercept)", "(Intercept)"] <- sig_0sq + total1 + total2 ## 2. Modifying without intercept updated_2 <- (sig_0isq)/sc - (small_vv %*% (ce/sc))/sc vv["(Intercept)", other_vars] <- updated_2 vv[other_vars, "(Intercept)"] <- updated_2 vv[other_vars, other_vars] <- vv[other_vars, other_vars] * outer(1/sc, 1/sc) vv <- as(vv, "dpoMatrix") } ##' Used for padding NAs to Cmat accordingly in predict.merMod ##' @param mat represents the matrix that needs to be modified ##' @param mat_names represents the names of the new modified matrix ##' @param insert_after represents the placement before the zeros that need to ##' be added ##' @param n_add represents the number rows/columns that will be padded with zeros zero_padding <- function(mat, mat_names, insert_after, n_add = 1) { mat <- as.matrix(mat) old_dim <- nrow(mat) new_dim <- old_dim + n_add m_pad <- matrix(0, new_dim, new_dim) rownames(m_pad) <- mat_names colnames(m_pad) <- mat_names ## Top right corner m_pad[1:insert_after, 1:insert_after] <- mat[1:insert_after, 1:insert_after] ## Top left corner m_pad[1:insert_after, (insert_after + n_add + 1):new_dim] <- mat[1:insert_after, (insert_after + 1):old_dim] ## Bottom right corner m_pad[(insert_after + n_add + 1):new_dim, 1:insert_after] <- mat[(insert_after + 1):old_dim, 1:insert_after] ## Bottom left corner m_pad[(insert_after + n_add + 1):new_dim, (insert_after + n_add + 1):new_dim] <- mat[(insert_after + 1):old_dim, (insert_after + 1):old_dim] m_pad } ##' if allow.new.levels = TRUE, then adds 0 padding to Cmat for prediction ##' @param Cmat represents Cmat that was computed prior to subsetting ##' @param C_factors represents the factors explicitly shown in Cmat ##' @param Z_factors represents the factors represented in the Z matrix, which ##' includes only levels of groups that need to be predicted ##' @param Cmat_names represents the names of the Cmat matrix ##' @param cnms same as cnms from object pad_Cmat <- function(Cmat, C_factors, Z_factors, Cmat_names, cnms){ n_padded = 0 for (grp in intersect(names(C_factors), names(Z_factors))) { n_lvl <- length(levels(C_factors[[grp]])) added_levels <- setdiff(levels(Z_factors[[grp]]), levels(C_factors[[grp]])) if ((n_add <- length(added_levels)) == 0) next levels(C_factors[[grp]]) <- c(levels(C_factors[[grp]]), added_levels) ## add names for clarity added_nms <- as.vector(sapply(added_levels, function(lv) paste0(grp, ".", lv, ".", cnms[[grp]]) )) ## add padding n_padded <- n_padded + n_lvl * length(cnms[[grp]]) n_new <- n_add * length(cnms[[grp]]) Cmat_names <- c(Cmat_names[1:n_padded], added_nms, Cmat_names[(n_padded+1):length(Cmat_names)]) ## alter Cmat Cmat <- zero_padding(Cmat, Cmat_names, insert_after = n_padded, n_add = n_new) n_padded <- n_padded + n_new } list("Cmat" = Cmat, "C_factors" = C_factors, "Cmat_names" = Cmat_names) } lme4/R/zzz.R0000644000176200001440000000745715113136605012312 0ustar liggesusers.onLoad <- function(libname, pkgname) { ## don't do this in production; also flags problems in downstream packages ## options(Matrix.warnDeprecatedCoerce = 3) options(lme4.summary.cor.max = 12) Ons <- parent.env(environment()) # our own namespace if((Rv <- getRversion()) < "4.4.0") { assign('%||%', envir = Ons, inherits = FALSE, function(x, y) if(is.null(x)) y else x) if(Rv < "4.1.0") { ## https://stackoverflow.com/questions/49056642/how-to-make-variable-available-to-namespace-at-loading-time/67664852#67664852 ## not quite equivalent; this *forces* ... entries whereas true ...names() doesn't assign('...names', envir = Ons, function() eval(quote(names(list(...))), sys.frame(-1L))) if(Rv < "4.0.0") { ## NB: R >= 4.0.0's deparse1() is a generalization of our previous safeDeparse() assign('deparse1', envir = Ons, function (expr, collapse = " ", width.cutoff = 500L, ...) paste(deparse(expr, width.cutoff, ...), collapse = collapse) ) ## not equivalent ... assign('...length', envir = Ons, function() eval(quote(length(list(...))), sys.frame(-1L)) ) if (Rv < "3.6.0") { assign('reformulate', envir = Ons, function(..., env = parent.env) { f <- stats::reformulate(...) environment(f) <- env f }) if (Rv < "3.2.1") { assign('lengths', envir = Ons, function (x, use.names = TRUE) vapply(x, length, 1L, USE.NAMES = use.names) ) if(Rv < "3.1.0") { assign('anyNA', envir = Ons, function(x) any(is.na(x)) ) if(Rv < "3.0.0") { assign('rep_len', envir = Ons, function(x, length.out) rep(x, length.out=length.out) ) if(Rv < "2.15") { assign('paste0', envir = Ons, function(...) paste(..., sep = '') ) } ## R < 2.15 } ## R < 3.0.0 } ## R < 3.1.0 } ## R < 3.2.1 } ## R < 3.6.0 } ## R < 4.0.0 } ## R < 4.1.0 } ## R < 4.4.0 rm(Rv, Ons) ## check Matrix ABI version check_dep_version() } ## https://github.com/lme4/lme4/issues/768 ## https://github.com/kaskr/adcomp/issues/387 get_abi_version <- function() { if (utils::packageVersion("Matrix") < "1.6-2") return(numeric_version("0")) Matrix::Matrix.Version()[["abi"]] } .Matrix.abi.build.version <- get_abi_version() ## simplified version of glmmTMB package checking ##' @param this_pkg downstream package being tested ##' @param dep_pkg upstream package on which \code{this_pkg} depends ##' @param dep_type "ABI" or "package" ##' @param built_version a \code{numeric_version} object indicating what version of \code{dep_pkg} was used to build \code{this_pkg} ##' @param warn (logical) warn if condition not met? ##' @noRd check_dep_version <- function(this_pkg = "lme4", dep_pkg = "Matrix", dep_type = "ABI", built_version = .Matrix.abi.build.version, warn = TRUE) { cur_version <- get_abi_version() result_ok <- cur_version == built_version if (!result_ok) { warning( sprintf("%s version mismatch: \n", dep_type), sprintf("%s was built with %s %s version %s\n", this_pkg, dep_pkg, dep_type, built_version), sprintf("Current %s %s version is %s\n", dep_pkg, dep_type, cur_version), sprintf("Please re-install %s from source ", this_pkg), "or restore original ", sQuote(dep_pkg), " package" ) } return(result_ok) } .onUnload <- function(libpath) { gc() if (is.loaded("lmer_Deviance", PACKAGE="lme4")) { library.dynam.unload("lme4", libpath) } } lme4/R/bootMer.R0000644000176200001440000002037715022107260013052 0ustar liggesusers.simpleCap <- function(x) { paste0(toupper(substr(x, 1,1)), substr(x, 2, 1000000L), collapse=" ") } ### bootMer() --- <==> (TODO: semi-*)parametric bootstrap ### ------------------------------------------------------- ## Doc: show how this is equivalent - but faster than ## boot(*, R = nsim, sim = "parametric", ran.gen = simulate(.,1,.), mle = x) ## --> return a "boot" object -- so we could use boot.ci() etc ## TODO: also allow "semi-parametric" model-based bootstrap: ## resampling the (centered!) residuals (for use.u=TRUE) or for use.u=FALSE, ## *both* the centered u's + centered residuals ## instead of using rnorm() ## BUT see: ## @article{morris_blups_2002, ## title = {The {BLUPs} are not "best" when it comes to bootstrapping}, ## volume = {56}, ## issn = {0167-7152}, ## url = {https://www.sciencedirect.com/science/article/pii/S016771520200041X}, ## doi = {10.1016/S0167-7152(02)00041-X}, ## journal = {Statistics \& Probability Letters}, ## author = {Morris, Jeffrey S}, ## year = {2002}, ## } ## for an indication of why this is not necessarily a good idea! ##' Perform model-based (Semi-)parametric bootstrap for mixed models. ##' ##' The semi-parametric variant is not yet implemented, and we only ##' provide a method for \code{\link{lmer}} and \code{\link{glmer}} results. bootMer <- function(x, FUN, nsim = 1, seed = NULL, use.u = FALSE, re.form = NA, type = c("parametric","semiparametric"), verbose = FALSE, .progress = "none", PBargs=list(), parallel = c("no", "multicore", "snow"), ncpus = getOption("boot.ncpus", 1L), cl = NULL) { stopifnot((nsim <- as.integer(nsim[1])) > 0) if (.progress!="none") { ## progress bar pbfun <- get(paste0(.progress,"ProgressBar")) setpbfun <- get(paste0("set",.simpleCap(.progress),"ProgressBar")) pb <- do.call(pbfun,PBargs) } do_parallel <- have_mc <- have_snow <- NULL # '-Wall', set here: eval(initialize.parallel) if (do_parallel && .progress != "none") message("progress bar disabled for parallel operations") FUN <- match.fun(FUN) type <- match.arg(type) if(!is.null(seed)) set.seed(seed) else if(!exists(".Random.seed", envir = .GlobalEnv)) runif(1) # initialize the RNG if necessary mc <- match.call() t0 <- FUN(x) if (!is.numeric(t0)) stop("bootMer currently only handles functions that return numeric vectors") mle <- list(beta = getME(x,"beta"), theta = getME(x,"theta")) if (isLMM(x)) mle <- c(mle,list(sigma = sigma(x))) ## FIXME: what about GLMMs with scale parameters?? ## FIXME: remove prefix when incorporated in package if (type=="parametric") { argList <- list(x, nsim=nsim, na.action=na.exclude) if (!missing(re.form)) { argList <- c(argList,list(re.form=re.form)) } else { argList <- c(argList,list(use.u=use.u)) } ss <- do.call(simulate,argList) } else { if (!missing(re.form)) stop(paste(sQuote("re.form")), "cannot be used with semiparametric bootstrapping") if (use.u) { if (isGLMM(x)) warning("semiparametric bootstrapping is questionable for GLMMs") ss <- replicate(nsim,fitted(x)+sample(residuals(x,"response"), replace=TRUE), simplify=FALSE) } else { stop("semiparametric bootstrapping with use.u=FALSE not yet implemented") } } ## FIXME:: use getCall(x) ? check for existence of slot? ## is control used except for merMod? control <- if (!is(x, "merMod")) NULL else eval.parent(x@call$control) # define ffun as a closure containing the referenced variables # in its scope to avoid explicit clusterExport statement # in the PSOCKcluster case ffun <- local({ FUN refit x ss verbose do_parallel control length.t0 <- length(t0) f1 <- factory(function(i) FUN(refit(x, ss[[i]], control = control)), errval = rep(NA, length.t0)) function(i) { ret <- f1(i) if (verbose) { cat(sprintf("%5d :",i)); str(ret) } if (!do_parallel && .progress!="none") { setpbfun(pb,i/nsim) } ret }}) simvec <- seq_len(nsim) res <- if (do_parallel) { if (have_mc) { parallel::mclapply(simvec, ffun, mc.cores = ncpus) } else if (have_snow) { if (is.null(cl)) { cl <- parallel::makePSOCKcluster(rep("localhost", ncpus)) ## explicit export of the lme4 namespace since most FUNs will probably ## use some of them parallel::clusterExport(cl, varlist=getNamespaceExports("lme4")) if(RNGkind()[1L] == "L'Ecuyer-CMRG") parallel::clusterSetRNGStream(cl) res <- parallel::parLapply(cl, simvec, ffun) parallel::stopCluster(cl) res } else parallel::parLapply(cl, simvec, ffun) } } else lapply(simvec, ffun) t.star <- do.call(cbind,res) rownames(t.star) <- names(t0) msgs <- list() for (mtype in paste0("factory-",c("message","warning","error"))) { msgs[[mtype]] <- trimws(unlist(lapply(res, attr, mtype))) msgs[[mtype]] <- table(msgs[[mtype]]) } if ((numFail <- sum(msgs[["factory-error"]])) > 0) { warning("some bootstrap runs failed (",numFail,"/",nsim,")") } fail.msgs <- if (numFail==0) NULL else msgs[["factory-error"]] ## mimic ending of boot() construction s <- structure(list(t0 = t0, t = t(t.star), R = nsim, data = model.frame(x), seed = .Random.seed, statistic = FUN, sim = "parametric", call = mc, ## these two are dummies ran.gen = "simulate(, 1, *)", mle = mle), class = c("bootMer", "boot")) ## leave these for back-compat attr(s,"bootFail") <- numFail attr(s,"boot.fail.msgs") <- fail.msgs attr(s,"boot.all.msgs") <- msgs ## store all messages (tabulated) attr(s,"boot_type") <- "boot" s } ## {bootMer} ##' @S3method as.data.frame boot as.data.frame.bootMer <- function(x,...) { as.data.frame(x$t) } ## FIXME: collapse convergence warnings (ignore numeric values ## when tabulating) ? print.bootWarnings <- function(x, verbose=FALSE, ...) { checkDots(..., .action = "warning") msgs <- attr(x, "boot.all.msgs") if (is.null(msgs) || all(lengths(msgs)==0)) { return(invisible(NULL)) } wstr <- "\n" for (i in c("message","warning","error")) { f <- paste0("factory-",i) m <- sort(msgs[[f]]) if (length(m)>0) { if (!verbose) { wstr <- c(wstr, paste0(sum(m)," ",i,"(s): ",names(m)[1])) if (length(m)>1) { wstr <- c(wstr," (and others)") } wstr <- c(wstr,"\n") } else { wstr <- paste0(i,"(s):\n") wstr <- c(wstr,capture.output(cat(cbind(" ",m,names(m)),sep="\n"))) wstr <- c(wstr,"\n") } } } message(wstr) return(invisible(NULL)) } print.bootMer <- function(x,...) { NextMethod(x,...) print.bootWarnings(x, verbose=FALSE) return(invisible(x)) } confint.bootMer <- function(object, parm=seq(length(object$t0)), level=0.95, type=c("perc","norm","basic"), ...) { type <- match.arg(type) bnms <- c(norm="normal",basic="basic",perc="percent") blens <- c(norm=3,basic=5,perc=5) bnm <- bnms[[type]] blen <- blens[[type]] btab0 <- t(vapply(parm, function(i) boot::boot.ci(object,index=i,conf=level, type=type)[[bnm]], FUN.VALUE=numeric(blen))) btab <- btab0[,(blen-1):blen,drop=FALSE] rownames(btab) <- names(object$t0) a <- (1 - level)/2 a <- c(a, 1 - a) ## replicate stats::format.perc pct <- paste(format(100 * a, trim = TRUE, scientific = FALSE, digits = 3), "%") colnames(btab) <- pct return(btab) } lme4/R/allFit.R0000644000176200001440000003165015113136605012660 0ustar liggesusersmeth.tab.0 <- cbind(optimizer= rep(c("bobyqa", "Nelder_Mead", "nlminbwrap", "nmkbw", "optimx", "nloptwrap" ), c(rep(1,5),2)), method= c(rep("",4), "L-BFGS-B", "NLOPT_LN_NELDERMEAD", "NLOPT_LN_BOBYQA")) ## ugh: hardcoded list (incomplete?) of allowable *control* options by optimizer ## could make more of an effort to match max-iterations/evals, ## (x|f)*(abs|rel) tolerance, ... opt.ctrls <- list(bobyqa=c("npt","rhobeg","rhoend","iprint","maxfun"), Nelder_Mead=c("iprint","maxfun","FtolAbs", "FtolRel","XtolRel","MinfMax", "xst","xt","verbose","warnOnly"), nlminbwrap=c("eval.max","iter.max","trace","abs.tol", "rel.tol","x.tol","xf.tol","step.min", "step.max","sing.tol","scale.init", "diff.g"), optimx=c("trace","fnscale","parscale","ndeps", "maxit","abstol","reltol","method"), nloptwrap=c("minf_max","ftol_rel","ftol_abs", "xtol_rel", "xtol_abs", "maxeval", "maxtime", "algorithm"), nmkbw=c("tol","maxfeval","regsimp","maximize", "restarts.max","trace","maxfun")) ## name of 'max function evaluations' for each optimizer maxfun_arg <- c(bobyqa = "maxfun", Nelder_Mead = "maxfun", nlminbwrap = "eval.max", optimx = "maxit", nloptwrap = "maxeval", nmkbw = "maxfeval") nmkbw <- function(fn, par, lower, upper, control) { if (length(par)==1) { res <- optim(fn=fn,par=par,lower=lower,upper=100*par, method="Brent") } else { res <- dfoptim::nmkb(fn=fn,par=par, lower=lower,upper=upper,control=control) } res$fval <- res$value res } ##' Attempt to re-fit a [g]lmer model with a range of optimizers. ##' The default is to use all known optimizers for R that satisfy the ##' requirements (do not require explicit gradients, allow ##' box constraints), in three categories; (i) built-in ##' (minqa::bobyqa, lme4::Nelder_Mead, nlminbwrap), (ii) wrapped via optimx ##' (most of optimx's optimizers that allow box constraints require ##' an explicit gradient function to be specified; the two provided ##' here are really base R functions that can be accessed via optimx, ##' (iii) wrapped via nloptr; (iv) ##' ##' @param object a fitted model ##' @param meth.tab a matrix (or data.frame) with columns ##' - method the name of a specific optimization method to pass to the optimizer ##' (leave blank for built-in optimizers) ##' - optimizer the \code{optimizer} function to use ##' @param verbose print progress messages? ##' @param catch.err catch errors? ##' @param start_from_mle (logical) initialize the refitting process with starting values from the fitted model? ##' @return a list of fitted \code{merMod} objects ##' @seealso slice, slice2D in the bbmle package ##' @examples ##' library(lme4) ##' gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), ##' data = cbpp, family = binomial) ##' gm_all <- allFit(gm1, parallel=TRUE) ##' ss <- summary(gm_all) ##' ss$fixef ## extract fixed effects ##' ss$llik ## log-likelihoods ##' ss$sdcor ## SDs and correlations ##' ss$theta ## Cholesky factors ##' ss$which.OK ## which fits worked allFit <- function(object, meth.tab = NULL, data=NULL, verbose=TRUE, show.meth.tab = FALSE, maxfun = 1e5, parallel = c("no", "multicore", "snow"), ncpus = getOption("allFit.ncpus", 1L), cl = NULL, catch.errs = TRUE, start_from_mle = TRUE) { if (is.null(meth.tab)) { meth.tab <- meth.tab.0 } optvec <- meth.tab.0[,"optimizer"] if (!requireNamespace("dfoptim", quietly = TRUE)) { optvec <- setdiff(optvec, "nmkbw") meth.tab <- meth.tab[meth.tab[,"optimizer"] %in% optvec, ] } if (!requireNamespace("optimx", quietly = TRUE)) { optvec <- setdiff(optvec, "optimx") meth.tab <- meth.tab[meth.tab[,"optimizer"] %in% optvec,] } if (show.meth.tab) { return(meth.tab) } stopifnot(length(dm <- dim(meth.tab)) == 2, dm[1] >= 1, dm[2] >= 2, is.character(optimizer <- meth.tab[,"optimizer"]), is.character(method <- meth.tab[,"method"])) parallel <- match.arg(parallel) do_parallel <- have_mc <- have_snow <- NULL # "-Wall" eval(initialize.parallel) # (parallel, ncpus) --> ./utilities.R ## |--> (do_parallel, have_mc, have_snow) fit.names <- gsub("\\.$", "", paste(optimizer, method, sep=".")) ffun <- local({ ## required local vars object verbose fit.names optimizer method maxfun function(..i) { if (verbose) cat(fit.names[..i],": ") ctrl <- getCall(object)$control ## NB: 'ctrl' must become a correct *argument* list for g?lmerControl() if(is.null(ctrl)) { ctrl <- list(optimizer=optimizer[..i]) } else { if(is.call(ctrl)) # typically true ctrl <- lapply(as.list(ctrl)[-1], eval) ctrl$optimizer <- optimizer[..i] } ## add method/algorithm to optCtrl as necessary mkOptCtrl <- function(...) { x <- list(...) cc <- ctrl$optCtrl if (is.null(cc)) cc <- list() cc[[maxfun_arg[[optimizer[[..i]]]]]] <- maxfun for (n in names(x)) { ## replace existing values cc[[n]] <- x[[n]] } cc } sanitize <- function(x,okvals) { if (is.null(x)) return(NULL) if (is.null(okvals)) return(x) x <- x[intersect(names(x),okvals)] ## ?? having names(control) be character(0) ## screws up nmkbw ... ?? if (length(names(x))==0) names(x) <- NULL x } ctrl$optCtrl <- switch(optimizer[..i], optimx = mkOptCtrl(method = method[..i]), nloptwrap = mkOptCtrl(algorithm= method[..i]), mkOptCtrl()) ctrl$optCtrl <- sanitize(ctrl$optCtrl, opt.ctrls[[optimizer[..i]]]) ctrl <- do.call(if(isGLMM(object)) glmerControl else lmerControl, ctrl) ## need to stick ctrl in formula env so it can be found ... form <- formula(object) env <- environment(form) tmp_env <- new.env(parent = env) # temporarily changing the environment environment(form) <- tmp_env assign("ctrl", ctrl, envir = tmp_env, inherits = FALSE) # Using the MLE as a starting point if (start_from_mle) { if (isGLMM(object)) { pars <- getME(object, c("theta", "fixef")) } else { pars <- getME(object, "theta") if(isNLMM(object)){ warning("results are not guaranteed when using nlmer") } } assign("pars", pars, envir = tmp_env, inherits = FALSE) } fit_call <- if (start_from_mle) { quote(update(object, start = pars, control = ctrl)) } else { quote(update(object, control = ctrl)) } tt <- system.time( rr <- if (catch.errs) { tryCatch(eval(fit_call), error = function(e) e) } else { eval(fit_call) } ) attr(rr, "optCtrl") <- ctrl$optCtrl # contains crucial info here attr(rr, "time") <- tt # store timing info ## restore original values to environment of the object on.exit({ environment(form) <- env }, add = TRUE) if (verbose) { if (inherits(rr,"error")) { cat("[failed]\n") } else { cat("[OK]\n") } } return(rr) } }) seq_fit <- seq_along(fit.names) res <- if (do_parallel) { if (have_mc) { parallel::mclapply(seq_fit, ffun, mc.cores = ncpus) } else if(have_snow) { if(is.null(cl)) { cl <- parallel::makePSOCKcluster(rep("localhost", ncpus)) ## consider exporting data/package ? ## parallel::clusterEvalQ(cl,library("lme4")) ## try to get data and export it? ## parallel::clusterExport(cl,??) res <- parallel::parLapply(cl, seq_fit, ffun) parallel::stopCluster(cl) res } else parallel::parLapply(cl, seq_fit, ffun) } else { warning("'do_parallel' is true, but 'have_mc' and 'have_snow' are not. Should not happen!") ## or stop() or we could silently use lapply(..) setNames(as.list(fit.names), fit.names) } } else lapply(seq_fit, ffun) names(res) <- fit.names structure(res, class = "allFit", fit = object, sessionInfo = sessionInfo(), data = data # is dropped if NULL ) } print.allFit <- function(x, width=80, ...) { cat("original model:\n") f <- attr(x,"fit") ss <- function(x) { if (nchar(x)>width) { strwrap(paste0(substr(x,1,width-3),"...")) } else x } ff <- ss(deparse1(formula(f))) cat(ff,"\n") cat("data: ",deparse(getCall(f)$data),"\n") cat("optimizers (",length(x),"): ", ss(paste(names(x),collapse=", ")),"\n", sep="") which.bad <- vapply(x,FUN=is,"error",FUN.VALUE=logical(1)) if ((nbad <- sum(which.bad))>0) { cat(nbad,"optimizer(s) failed\n") } cat("differences in negative log-likelihoods:\n") nllvec <- -vapply(x[!which.bad],logLik,numeric(1)) cat("max=",signif(max(nllvec-min(nllvec)),3), "; std dev=",signif(sd(nllvec),3), "\n") ## FIXME: print magnitudes of parameter diffs ## cat("differences in parameters:\n") ## ss <- summary(x) ## allpars <- cbind(ss$fixef, ss$sdcor) ## par_max <- invisible(x) } summary.allFit <- function(object, ...) { afun <- function(x, FUN, ...) { f1 <- FUN(x[[1]], ...) nm <- names(f1) n <- length(f1) res <- vapply(x, FUN, numeric(n), ...) if (!is.null(dim(res))) { res <- t(res) } else { res <- as.matrix(res) colnames(res) <- nm } res } which.OK <- !vapply(object, is, "error", FUN.VALUE=logical(1)) objOK <- object[which.OK] msgs <- lapply(objOK, function(x) x@optinfo$conv$lme4$messages) fixef <- afun(objOK, fixef) llik <- vapply(objOK, logLik, numeric(1)) times <- afun(objOK, attr, "time") feval <- vapply(objOK, function(x) x@optinfo$feval, numeric(1)) vfun <- function(x) as.data.frame(VarCorr(x))[["sdcor"]] sdcor <- afun(objOK, vfun) theta <- afun(objOK, getME, name="theta") cnm <- tnames(objOK[[1]]) if (sigma(objOK[[1]])!=1) cnm <- c(cnm,"sigma") colnames(sdcor) <- unname(cnm) sdcor <- as.data.frame(sdcor) res <- namedList(which.OK, msgs, fixef, llik, sdcor, theta, times, feval) class(res) <- "summary.allFit" res } ## should add a print method for summary: ## * fixed effects, random effects: summary of differences? ## not yet ... plot.allFit <- function(x, abbr=16, ...) { values <- opt <- NULL ## R code check/non-standard evaluation if (! (requireNamespace("ggplot2"))) { stop("ggplot2 package must be installed to plot allFit objects") } aes <- NULL ## code check ss <- summary(x) ff <- stack(as.data.frame(ss$fixef)) ff$opt <- rep(rownames(ss$fixef),length.out=nrow(ff)) if (!is.null(abbr)) ff$opt <- abbreviate(ff$opt, minlength=abbr) (ggplot2::ggplot(ff, aes(values, opt, colour=opt)) + ggplot2::geom_point() + ggplot2::facet_wrap(~ind,scale="free") + ggplot2::theme(legend.position="none") ) } lme4/R/methods.R0000644000176200001440000002266415103163201013104 0ustar liggesusersinfluence.merMod <- function(model, groups, data, maxfun=1000, do.coef = TRUE, ncores=getOption("mc.cores",1), start=NULL, ...) { .groups <- NULL ## avoid false-positive code checks .vcov <- function(x) Matrix::as.matrix(vcov(x)) if (...length()>0) warning("disregarded additional arguments") if (!do.coef) { ## simple/quick/trivial results result <- list(hat=hatvalues(model)) class(result) <- "influence.merMod" return(result) } if (missing(data)) { data <- getCall(model)$data data <- if (!is.null(data)) eval(data, parent.frame()) else stop("model did not use the data argument") } data <- as.data.frame(data) ## prevent tibble trouble if (missing(groups)) { groups <- ".case" data$.case <- rownames(data) } else if (length(groups) > 1){ del.var <- paste0(groups, collapse=".") data[, del.var] <- apply(data, 1, paste0, collapse=".") groups <- del.var } unique.del <- unique(data[, groups]) data[[".groups"]] <- data[, groups] if (!is.null(start)) { par <- start } else { par <- list(theta=getME(model, "theta")) if (inherits(model, "glmerMod") && getME(model, "devcomp")$dims[["nAGQ"]]>0) { par$fixef <- fixef(model) } } fixed <- fixef(model) fixed.1 <- matrix(0, length(unique.del), length(fixed)) rownames(fixed.1) <- unique.del colnames(fixed.1) <- names(fixed) Vs <- VarCorr(model) nms <- names(Vs) sep <- ":" if (length(nms) == 1) { nms <- "" sep <- "" } vc <- getME(model, "sigma")^2 names(vc) <- "sigma^2" for (i in 1:length(Vs)){ V <- Vs[[i]] c.names <- colnames(V) e.names <- outer(c.names, c.names, function(a, b) paste0("C[", a, ",", b, "]")) diag(e.names) <- paste0("v[", c.names, "]") v <- V[lower.tri(V, diag=TRUE)] names(v) <- paste0(nms[i], sep, e.names[lower.tri(e.names, diag=TRUE)]) vc <- c(vc, v) } vc.1 <- matrix(0, length(unique.del), length(vc)) rownames(vc.1) <- unique.del colnames(vc.1) <- names(vc) feval <- numeric(length(unique.del)) converged <- logical(length(unique.del)) vcov.1 <- vector(length(unique.del), mode="list") names(vcov.1) <- names(feval) <- names(converged) <- unique.del # control <- if (one.step){ # if (inherits(model, "lmerMod")) lmerControl(optimizer="optimx", optCtrl=list(method="L-BFGS-B", maxit=1)) # else if (inherits(model, "glmerMod")) glmerControl(optimizer="optimx", optCtrl=list(method="L-BFGS-B", maxit=1)) # } else { control <- if (inherits(model, "lmerMod")) lmerControl(optCtrl=list(maxfun=maxfun)) else if (inherits(model, "glmerMod")) glmerControl(optCtrl=list(maxfun=maxfun)) # } deleteGroup <- function(del) { data$del <- del mod.1 <- suppressWarnings(update(model, data=data, subset=(.groups != del), start=par, control=control)) opt <- mod.1@optinfo feval <- opt$feval converged <- opt$conv$opt == 0 && length(opt$warnings) == 0 fixed.1 <- fixef(mod.1) Vs.1 <- VarCorr(mod.1) vc.0 <- getME(mod.1, "sigma")^2 for (V in Vs.1){ vc.0 <- c(vc.0, V[lower.tri(V, diag=TRUE)]) } vc.1 <- vc.0 vcov.1 <<- .vcov(mod.1) namedList(fixed.1, vc.1, vcov.1, converged, feval) } result <- if(ncores >= 2) { message("Note: using a cluster of ", ncores, " cores") cl <- parallel::makeCluster(ncores) on.exit(parallel::stopCluster(cl)) parallel::clusterEvalQ(cl, require("lme4")) parallel::clusterApply(cl, unique.del, deleteGroup) } else { lapply(unique.del, deleteGroup) } result <- combineLists(result) fixed.1 <- result$fixed.1 rownames(fixed.1) <- unique.del colnames(fixed.1) <- names(fixed) vc.1 <- result$vc.1 rownames(vc.1) <- unique.del colnames(vc.1) <- names(vc) feval <- as.vector(result$feval) converged <- as.vector(result$converged) vcov.1 <- result$vcov.1 names(vcov.1) <- names(feval) <- names(converged) <- unique.del left <- "[-" right <- "]" if (groups == ".case") { groups <- "case" } nms <- c("fixed.effects", paste0("fixed.effects", left, groups, right), "var.cov.comps", paste0("var.cov.comps", left, groups, right), "vcov", paste0("vcov", left, groups, right), "groups", "deleted", "converged", "function.evals") result <- list(fixed, fixed.1, vc, vc.1, .vcov(model), vcov.1, groups, unique.del, converged, feval) names(result) <- nms class(result) <- "influence.merMod" result } ## lookup table for influence.merMod elements ipos <- c("fixed"=1, "fixed.sub"=2, "var.cov"=3, "var.cov.sub"=4, "vcov"=5, "vcov.sub"=6) dfbeta.influence.merMod <- function(model, which=c("fixed", "var.cov"), ...){ which <- match.arg(which) b <- model[[ipos[sprintf("%s.sub",which)]]] b0 <- model[[ipos[which]]] b - matrix(b0, nrow=nrow(b), ncol=ncol(b), byrow=TRUE) } dfbetas.influence.merMod <- function(model, ...){ vList <- model[[ipos["vcov.sub"]]] n <- nrow(vList[[1]]) vmat <- t(vapply(vList, function(x) sqrt(diag(x)), numeric(n))) dfbeta(model)/vmat } cooks.distance.merMod <- function(model, ...) { p <- Matrix::rankMatrix(getME(model,"X")) hat <- hatvalues(model) dispersion <- sigma(model)^2 res <- residuals(model,type="pearson") res <- (res/(1 - hat))^2 * hat/(dispersion * p) res[is.infinite(res)] <- NaN res } cooks.distance.influence.merMod <- function(model, ...) { db <- dfbeta(model) n <- nrow(db) p <- ncol(db) d <- numeric(n) names(d) <- names(model[["vcov[-case]"]]) vcov.inv <- (n - p)/(n*p)*solve(model$vcov) for (i in 1:n) { d[i] <- db[i, ] %*% vcov.inv %*% db[i, ] } d } ## from ?lm.influence: ## wt.res: a vector of _weighted_ (or for class 'glm' rather _deviance_) ## residuals. ## ## residuals.lm gives r*sqrt(object$weights) (if non-NULL weights) ## for type %in% c("deviance","pearson") ## ## residuals.glm gives (y - mu) * sqrt(wts)/sqrt(object$family$variance(mu)) ## rstudent.merMod <- function (model, ...) { r <- residuals(model, type="deviance") hat <- hatvalues(model) pr <- residuals(model, type="pearson") r <- sign(r) * sqrt(r^2 + (hat * pr^2)/(1 - hat)) r[is.infinite(r)] <- NaN r/sigma(model) } ##' @S3method sigma merMod sigma.merMod <- function(object, ...) { dc <- object@devcomp dd <- dc$dims if(dd[["useSc"]]) dc$cmp[[if(dd[["REML"]]) "sigmaREML" else "sigmaML"]] else 1. } ##' @importFrom stats terms ##' @S3method terms merMod terms.merMod <- function(x, fixed.only=TRUE, random.only=FALSE, ...) { if (missing(fixed.only) && random.only) fixed.only <- FALSE if (fixed.only && random.only) stop("can't specify 'only fixed' and 'only random' terms") tt <- attr(x@frame,"terms") if (fixed.only) { ## ... may contain 'data' (needed when formula contains .) tt <- terms.formula(formula(x,fixed.only=TRUE), ...) attr(tt,"predvars") <- attr(terms(x@frame),"predvars.fixed") } if (random.only) { tt <- terms.formula(reformulas::subbars(formula(x,random.only=TRUE))) ## FIXME: predvars should be random-only attr(tt,"predvars") <- attr(terms(x@frame),"predvars.random") } tt } ##' @importFrom stats update ##' @S3method update merMod update.merMod <- function(object, formula., ..., evaluate = TRUE) { if (is.null(call <- getCall(object))) stop("object should contain a 'call' component") extras <- match.call(expand.dots = FALSE)$... if (!missing(formula.)) call$formula <- update.formula(formula(object), formula.) if (length(extras) > 0) { existing <- !is.na(match(names(extras), names(call))) for (a in names(extras)[existing]) call[[a]] <- extras[[a]] if (any(!existing)) { call <- c(as.list(call), extras[!existing]) call <- as.call(call) } } if (!evaluate) return(call) ## should be able to find model components somewhere in (1) formula env; (2) calling env; ## (3) parent frame [plus its parent frames] ## see discusion at https://stackoverflow.com/questions/64268994/evaluate-call-when-components-may-be-scattered-among-environments ## FIXME: suppressWarnings(update(model)) will give ## Error in as.list.environment(X[[i]], ...) : ## promise already under evaluation: recursive default argument reference or earlier problems? ff <- environment(formula(object)) pf <- parent.frame() sf <- sys.frames()[[1]] tryCatch(eval(call, envir = ff), ## try formula environment error = function(e) { tryCatch(eval(call, envir = sf), ## try stack frame error = function(e) { eval(call, envir=pf) ## try parent frame }) }) ## ## combf <- tryCatch( ## do.call("c", lapply(list(ff, sf), as.list)), ## error=function(e) as.list(ff) ## ) ## eval(call,combf, enclos=pf) } lme4/R/lmerControl.R0000644000176200001440000002063715103764661013760 0ustar liggesusersnamedList <- function(...) { L <- list(...) snm <- sapply(substitute(list(...)), deparse)[-1] if (is.null(nm <- names(L))) nm <- snm if (any(nonames <- nm == "")) nm[nonames] <- snm[nonames] setNames(L,nm) } ##' By extracting "checking options" from \code{nms}, this function ##' implicitly defines what "checking options" are. ##' ##' @title Extract "checking options" or "checking arguments" (-> ../man/lmerControl.Rd) ##' those starting with "check." but *not* the "check.conv.." nor "check.rankX.." ones: ##' @param nms character vector ##' @return those elements of \code{nms} which are "checking options" ##' @author Martin Maechler .get.checkingOpts <- function(nms) nms[grepl("^check\\.(?!conv|rankX|scaleX)", nms, perl=TRUE)] ##' Check check.conv.*() options and produce good error message chk.convOpt <- function(opt) { cnm <- deparse(nm <- substitute(opt))[[1]] if(!is.list(opt)) stop("check.conv* option ", cnm, " must be a list") if(!is.character(opt$action)) stop("check.conv* option ", cnm, " has no 'action' string") if(!is.numeric(tol <- opt$tol)) stop("check.conv* option ", cnm, " must have a numeric 'tol' component") if(length(tol) != 1 || tol < 0) stop("check.conv* option ", cnm, "$tol must be number >= 0") if(!is.null(relTol <- opt$relTol)) stopifnot(is.numeric(relTol), length(relTol) == 1, relTol >= 0) invisible() } ##' Exported constructor for the user calling *lmerControl(): .makeCC <- function(action, tol, relTol, ...) { stopifnot(is.character(action), length(action) == 1) if(!is.numeric(tol)) stop("must have a numeric 'tol' component") if(length(tol) != 1 || tol < 0) stop("'tol' must be number >= 0") mis.rt <- missing(relTol) if(!mis.rt && !is.null(relTol)) stopifnot(is.numeric(relTol), length(relTol) == 1, relTol >= 0) ## and return the list, the "..." just being appended unchecked c(list(action = action, tol = tol), if(!mis.rt) list(relTol = relTol), list(...)) } ##' Internal utility : Allow check.conv.* to be a string chk.cconv <- function(copt, callingFn) { cnm <- deparse(substitute(copt)) if(is.character(copt)) { def <- eval(formals(callingFn)[[cnm]]) def$action <- copt assign(cnm, def, envir=sys.frame(sys.parent())) } else chk.convOpt(copt) } ## work around code check since we adjust formals() later check.response.not.const <- compDev <- nAGQ0initStep <- tolPwrss <- NULL merControl <- function(optimizer="nloptwrap", # originally Nelder_Mead, then bobyqa ... ## glmer: c("bobyqa","Nelder_Mead") restart_edge=TRUE, ## glmer: FALSE ## don't call any of these arguments "check.*" -- will fail ## automatic check-option-checking in ## inst/tests/test-lmer.R boundary.tol=1e-5, calc.derivs=NULL, use.last.params=FALSE, sparseX=FALSE, standardize.X=FALSE, autoscale=NULL, ## input checking options: check.nobs.vs.rankZ="ignore", ## "warningSmall", check.nobs.vs.nlev="stop", check.nlev.gtreq.5="ignore", check.nlev.gtr.1="stop", check.nobs.vs.nRE="stop", check.rankX = c("message+drop.cols", "silent.drop.cols", "warn+drop.cols", "stop.deficient", "ignore"), check.scaleX = c("warning","stop","silent.rescale", "message+rescale","warn+rescale","ignore"), check.formula.LHS = "stop", ## convergence options check.conv.nobsmax = 1e4, check.conv.nparmax = 10, check.conv.grad = .makeCC("warning", tol = 2e-3, relTol = NULL), check.conv.singular = .makeCC(action = "message", tol = getSingTol()), check.conv.hess = .makeCC(action = "warning", tol = 1e-6), optCtrl = list(), mod.type="lmer" ## glmer: "glmer" ## glmer: tolPwrss=1e-7 ## compDev = TRUE ## nAGQ0initStep = TRUE ## check.response.not.const="stop" ) { ## FIXME: is there a better idiom? match.call() ? ## fill in values from options, but **only if not specified explicitly in arguments** ## (ugh ... is there a better way to do this? mapply() is clunky: ## http://stackoverflow.com/questions/16276667/using-apply-with-assign-in-r stopifnot(is.list(optCtrl)) if (mod.type=="glmer" && length(optimizer)==1) { ## replicate() works whether 'optimizer' is a function or a string optimizer <- replicate(2,optimizer) } c.opts <- paste0(mod.type,"Control") merOpts <- getOption(c.opts) if (!is.null(merOpts)) { nn <- names(merOpts) nn.ok <- .get.checkingOpts(names(merOpts)) if (length(nn.ignored <- setdiff(nn,nn.ok))>0) { warning("some options in ",shQuote(sprintf("getOption('%s')",c.opts)), " ignored : ",paste(nn.ignored,collapse=", ")) } for (arg in nn.ok) { if (do.call(missing,list(arg))) ## only if missing from explicit arguments assign(arg,merOpts[[arg]]) } } check.rankX <- match.arg(check.rankX) check.scaleX <- match.arg(check.scaleX) ## compatibility and convenience, caller can specify action string only: me <- sys.function() chk.cconv(check.conv.grad, me) chk.cconv(check.conv.singular, me) chk.cconv(check.conv.hess , me) if (mod.type=="glmer" && use.last.params && calc.derivs) { warning("using ",shQuote("use.last.params"),"=TRUE and ", shQuote("calc.derivs"),"=TRUE with ",shQuote("glmer"), " will not give backward-compatible results") } ret <- namedList(optimizer, restart_edge, boundary.tol, calc.derivs, use.last.params, checkControl = namedList(autoscale, check.nobs.vs.rankZ, check.nobs.vs.nlev, check.nlev.gtreq.5, check.nlev.gtr.1, check.nobs.vs.nRE, check.rankX, check.scaleX, check.formula.LHS), checkConv= namedList(check.conv.nobsmax, check.conv.nparmax, check.conv.grad, check.conv.singular, check.conv.hess), optCtrl=optCtrl) if (mod.type=="glmer") { ret <- c(ret, namedList(tolPwrss, compDev, nAGQ0initStep)) ret$checkControl <- c(ret$checkControl, namedList(check.response.not.const)) } class(ret) <- c(c.opts, "merControl") ret } lmerControl <- merControl glmerControl <- merControl formals(glmerControl)[["check.conv.nparmax"]] <- 20 formals(glmerControl)[["optimizer"]] <- c("bobyqa","Nelder_Mead") formals(glmerControl)[["mod.type"]] <- "glmer" formals(glmerControl)[["restart_edge"]] <- FALSE formals(glmerControl) <- c(formals(glmerControl), list(tolPwrss=1e-7, compDev = TRUE, nAGQ0initStep = TRUE, check.response.not.const="stop") ) ##' @rdname lmerControl ##' @export nlmerControl <- function(optimizer="Nelder_Mead", tolPwrss = 1e-10, optCtrl = list()) { stopifnot(is.list(optCtrl)) if (length(optimizer)==1) { optimizer <- replicate(2,optimizer) } structure(namedList(optimizer, tolPwrss, optCtrl=optCtrl), class = c("nlmerControl", "merControl")) } lme4/R/deriv.R0000644000176200001440000000674315022107260012555 0ustar liggesusers ##' This function computes the 1st and 2nd derivative (gradient and ##' Hessian) of a function ##' with vector-valued input and scalar output using a central finite ##' difference method. ##' ##' The function has to return a single scalar numerical value and take ##' as argument a numeric vector of the same length as \code{x}. The function also ##' has to be evaluable in a neighborhood around \code{x}, i.e. at ##' \code{x - delta} and at \code{x + delta} for all elements in \code{x}. ##' ##' @title Compute 1st and 2nd derivative ##' @param fun function for which to compute the gradient. ##' @param x numeric vector of values at which to compute the ##' gradient. ##' @param delta the amount to subtract and add to \code{x} when computing the ##' central difference. ##' @param fx optional value of \code{fun(x, ...)}. ##' @param ... additional arguments to \code{fun}. ##' ##' @keywords utilities ##' @export ##' @family derivatives ##' ##' @return a list with components ##' \item{gradient}{the first derivative vector} ##' \item{Hessian}{the second derivative matrix} ##' @author Rune Haubo Bojesen Christensen ##' deriv12 <- function(fun, x, delta=1e-4, fx=NULL, lower=rep(NA,length(x)), upper=rep(NA,length(x)), ...) { ### Compute gradient and Hessian simultaneously (to save computing time) nx <- length(x) if(is.null(fx)) fx <- fun(x, ...) stopifnot(length(fx) == 1, nx >= 1) H <- array(NA_real_, dim=c(nx, nx)) g <- numeric(nx) xadd <- x + delta hasUB <- !missing(upper) && any(active <- !is.na(upper)) if(hasUB) { # only then may have non-NA if((hasUB <- any(active <- active & xadd > upper))) { udelta <- ifelse(active, upper-x, delta) xadd[active] <- upper[active] } } xsub <- x - delta hasLB <- !missing(lower) && any(active <- !is.na(lower)) if(hasLB) { # only then may have non-NA if((hasLB <- any(active <- active & xsub < lower))) { ldelta <- ifelse(active, x-lower, delta) xsub[active] <- lower[active] } } ## substitute elements of 'mod' vectors into position(s) 'pos' ## in base spos <- function(base,mod,pos) { if (is.list(mod)) { for (i in seq_along(mod)) { base <- spos(base,mod[[i]],pos[i]) } base } else { base[pos] <- mod[pos] base } } for(j in 1:nx) { ## Diagonal elements: fadd <- fun(spos(x,xadd,j), ...) fsub <- fun(spos(x,xsub,j), ...) udj <- if(hasUB) udelta[j] else delta ldj <- if(hasLB) ldelta[j] else delta H[j, j] <- fadd/udj^2 - 2 * fx/(udj*ldj) + fsub/ldj^2 g[j] <- (fadd - fsub) / (udj+ldj) # "total delta" (udelta+ldelta)[j] ## Off diagonal elements: for(i in seq_len(j - 1L)) { # 1 <= i < j ## Compute upper triangular elements (and mirror to lower tri.): ud.i <- if(hasUB) udelta[i] else delta ld.i <- if(hasLB) ldelta[i] else delta xaa <- spos(x,list(xadd,xadd),c(i,j)) xas <- spos(x,list(xadd,xsub),c(i,j)) xsa <- spos(x,list(xsub,xadd),c(i,j)) xss <- spos(x,list(xsub,xsub),c(i,j)) H[i, j] <- H[j, i] <- fun(xaa, ...)/(ud.i+udj)^2 - fun(xas, ...)/(ud.i+ldj)^2 - fun(xsa, ...)/(ld.i+udj)^2 + fun(xss, ...)/(ld.i+ldj)^2 } } list(gradient = g, Hessian = H) } lme4/R/rePCA.R0000644000176200001440000000167415022107260012374 0ustar liggesusers##' PCA of random-effects variance-covariance estimates ##' ##' Perform a Principal Components Analysis (PCA) of the random-effects ##' variance-covariance estimates from a fitted mixed-effects model ##' @title PCA of random-effects ##' @param x a merMod object ##' @return a \code{prcomplist} object ##' @author Douglas Bates ##' @export rePCA <- function(x) UseMethod('rePCA') #' @export rePCA.merMod <- function(x) { chfs <- getME(x,"Tlist") # list of lower Cholesky factors nms <- names(chfs) unms <- unique(nms) names(unms) <- unms svals <- function(m) { vv <- svd(m,nv=0L) names(vv) <- c("sdev","rotation") vv$center <- FALSE vv$scale <- FALSE class(vv) <- "prcomp" vv } structure(lapply(unms,function(m) svals(Matrix::bdiag(chfs[which(nms == m)]))), class="prcomplist") } #' @export summary.prcomplist <- function(object,...) { lapply(object,summary) } lme4/R/GHrule.R0000644000176200001440000000224015022107260012616 0ustar liggesusers##' Create a univariate Gauss-Hermite quadrature rule ##' ##' This version of Gauss-Hermite quadrature provides the node ##' positions and weights for a scalar integral of a function ##' multiplied by the standard normal density. GHrule <- function (ord, asMatrix=TRUE) { stopifnot(length(ord) == 1, (ord <- as.integer(ord)) >= 0L, ord < 101L) if (ord == 0L) { if (asMatrix) return(matrix(0, nrow=0L, ncol=3L)) stop ("combination of ord==0 and asMatrix==TRUE not implemented") } ## fgq_rules comes from sysdata.rda: ## result of ## library("fastGHQuad") ## rescale <- function(x, scale.weights=TRUE, scale.roots=TRUE) { ## within(x, { ## if (scale.weights) w <- w/sum(w) ## if (scale.roots) x <- x*sqrt(2) ## }) ## } ## fgqRules <- lapply(1:100, function(n) setNames(rescale(gaussHermiteData(n)), c("z", "w"))) ## ## However, this shows small differences !! ## all.equal(lme4:::fgq_rules, fgqRules, tol=0) fr <- as.data.frame(fgq_rules[[ord]]) rownames(fr) <- NULL fr$ldnorm <- dnorm(fr$z, log=TRUE) if (asMatrix) as.matrix(fr) else fr } lme4/R/sparsegrid.R0000644000176200001440000000263715022107260013605 0ustar liggesusers## Generate sparse multidimensional Gaussian quadrature grids ---> ../man/GQdk.Rd ## Unused currently; rather GHrule() --> ./GHrule.R GQdk <- function(d=1L, k=1L) { stopifnot(0L < (d <- as.integer(d)[1]), d <= 20L, 0L < (k <- as.integer(k)[1]), k <= length(GQNd <- GQN[[d]]))## -> GQN, stored in ./sysdata.rda tmat <- t(GQNd[[k]]) ##rperms<- combinat::permn(seq_len(d) + 1L, function(v) c(1L,v)) ## rperms <- lapply(.Call(allPerm_int, seq_len(d) + 1L), function(v) c(1L, v)) perms <- tryCatch ( .Call(allPerm_int, seq_len(d) + 1L, as.integer(factorial(d))), warning = function (w) w, error = function (e) e) if (methods::is(perms, "error") | methods::is(perms, "warning")) stop("Can not allocate a vector that large") rperms <- lapply(perms, function(v) c(1L, v)) dd <- unname(as.matrix(do.call(expand.grid, c(rep.int(list(c(-1,1)), d), KEEP.OUT.ATTRS=FALSE)))) #unname(unique(t(do.call(cbind, # lapply(as.data.frame(t(cbind(1, dd))), # "*", e2=do.call(cbind, lapply(rperms, function(ind) tmat[ind,]))))))) e2 <- do.call(cbind, lapply(rperms, function(ind) tmat[ind,])) ddf <- as.data.frame(t(cbind(1,dd))) res <- NULL for (i in 1:ncol(ddf)) res <- unique(rbind(res, t(ddf[, i] * e2))) return(res) } lme4/R/sysdata.rda0000644000176200001440000162111015022107260013451 0ustar liggesusersý7zXZi"Ţ6!ĎXĚâlďţ])TW"änRĘź’Řâ •ˇá°,ľďÁŐćboż*m§ŕ =îFáżžŢqۡWJ/€«€Ťm˙[ţ>]XIRHśm…#ş´4‚Ce 3ö«ýцݺ#64K‹źAˇ™˘ RtUŐƨ´%YT°˝HĽą4-çPěs‘_Ű4?ŤgŇŇ'×5µ`ĆđwĽ˝¦ôŻJk›8v«ąÉÜpąčČäfôönŔbYť€Ô–-!®,äđĂ©!`Â=ŕĺmŘÄĘ·=$űG‹(#:ĘpťĎ?ř]üöXđ5­3ĆŹ¬Ť1ŻAĽµ4,ź4C¦˙Ç›ÜíŮ®”žłAP‡Ýl+ą-¶®‘űJ‘ă:Öćq«Ëk¸L)>âN t:PË‘ŕNZřńǵ>8řÄÇî´ň (éODţčßh=xNSqđ†­k.RV˙­qPgĽŔćru`h}źŠŻ`l¦g„*t/đ¨ź6šÄąĘ&őŕńJ^DTżąłD™ řę2a€ź# íĄt†ťđ«čÇ4něe'ŹNc[ľF† }żó5L"=Ă*Ë˝ě¦ŰrŇcśť”ádîú@6C”‘>Fč‘ŮZFd]Nć:BÖ O©ÜUĄü€Îl 9Ľ:™TťňŢýr ¬ëµţvŞßMĽ˙h*T|wĽÁĺ÷ŤŮ»”Ę ) xÓ‘*IVB>RŢB×—šżÁŹ ŐçźP™,Ë”±-ŻĂÝ+o=ţr ·K)ÓŽ+ű‡}f2Ř"iRYhy% ć(ŃRórŰü´Đ!őţ&NˇS¸ŠÇ„ënŻi´>öÓýC6 ¦¶u¸@mż‰P¶o=ÔîÖÉM|•.ü‰S !ŕúDßó¬˘ű§ánc=ÉIűş›cÁp8¬Ž"_¤8p’čמÂUŤäĐQĹ©fŇ·§rN“€gw´"vŤQzŮOŰĐe™+.łűaě—ÂÚ­@©ńäŠßĄĹ{´4ÇjĘ#·.Ź 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%śî|ŘsúÉFá÷ćţÖÍŽ’‘‹%YólČČ0E‰© 2q”¤) Ňăq1Í97ěrý-“äĐ­$ˇâŔ¦ßľKŠhŠŘ$ÄŮ–Żíü:Č$†ŐcŻ箩Ńq•qÓ‘wS° 2Y‰íckď!·˝üŽ Ź ŁŐ“˙zn şż ®Á…S»5Ľ,[ŐÓ×Ň۵ěea‡ýŕ‹Âł÷ŐÇč¸^ĎăćőbÓ0A‹ŤQeť8W°Ń1Bęľl(ĘůéuKů»‰cDřNBŠö&+ŤŘk¤h,Ä.ŔŢ aŮŹbuď뇣ťp( ŕ“󄢄6¶W{‰ë~Ô7ńXłű†­SŁÚuN±Ú˝ŤěQ3$ŘG$ş·3˘%TIëH›θqµÁq"°őż”äCÜ S~·v)µŻP˝—wu­¤×ą§HBâ¨zâÔ¸m»°żň DćśĂ±]q}M/bg ^Ą7ă´ĄL42( ZŽĆŇ f[›ÔgE*’ôţ®šn÷üçâZË {˝]/u˘+-vłłŞ/‡ßYµôĘPţzxŐ|U ?Ă˝`> 뼌‹ËD§çľLM"©ą¨dÇË^ń 0”o­×ăĘ#E&ž oyGý27 łgîU«>ůŕ<Č›ňç€\±°¬ #é— Ä÷…Ąá“ă2›ăQ@YZlme4/R/lmer.R0000644000176200001440000034123215113136610012400 0ustar liggesusers## NB: doc in ../man/*.Rd ***not*** auto generated ## FIXME: need to document S3 methods better (can we pull from r-forge version?) ##' Fit a linear mixed model (LMM) lmer <- function(formula, data=NULL, REML = TRUE, control = lmerControl(), start = NULL , verbose = 0L , subset, weights, na.action, offset , contrasts = NULL , devFunOnly=FALSE ) ## , ...) { mc <- mcout <- match.call() missCtrl <- missing(control) ## see functions in modular.R for the body .. if (!missCtrl && !inherits(control, "lmerControl")) { if(!is.list(control)) stop("'control' is not a list; use lmerControl()") ## back-compatibility kluge warning("passing control as list is deprecated: please use lmerControl() instead", immediate.=TRUE) control <- do.call(lmerControl, control) } ## if (!is.null(list(...)[["family"]])) { ## warning("calling lmer with 'family' is deprecated; please use glmer() instead") ## mc[[1]] <- quote(lme4::glmer) ## if(missCtrl) mc$control <- glmerControl() ## return(eval(mc, parent.frame(1L))) ## } mc$control <- control ## update for back-compatibility kluge ## https://github.com/lme4/lme4/issues/50 ## parse data and formula mc[[1]] <- quote(lme4::lFormula) lmod <- eval(mc, parent.frame(1L)) mcout$formula <- lmod$formula lmod$formula <- NULL if (is.matrix(y <- model.response(lmod$fr)) && ncol(y) > 1) { stop("can't handle matrix-valued responses: consider using refit()") } ## create deviance function for covariance parameters (theta) devfun <- do.call(mkLmerDevfun, c(lmod, list(start=start, verbose=verbose, control=control))) if (devFunOnly) return(devfun) ## optimize deviance function over covariance parameters s <- getStart(start, environment(devfun)$pp) if (identical(control$optimizer,"none")) stop("deprecated use of optimizer=='none'; use NULL instead") ## Checks if the user actually enabled calc.derivs force.calc.derivs <- isTRUE(control$calc.derivs) calc.derivs <- control$calc.derivs %||% (nrow(lmod$fr) < control$checkConv$check.conv.nobsmax & length(s) < control$checkConv$check.conv.nparmax) opt <- if (length(control$optimizer)==0) { list(par=s,fval=devfun(s), conv=1000,message="no optimization") } else { optimizeLmer(devfun, optimizer = control$optimizer, restart_edge = control$restart_edge, boundary.tol = control$boundary.tol, control = control$optCtrl, verbose=verbose, start=start, calc.derivs=calc.derivs, force.calc.derivs=force.calc.derivs, use.last.params=control$use.last.params) } cc <- checkConv(attr(opt,"derivs"), opt$par, ctrl = control$checkConv, lbound = environment(devfun)$lower, nobs = nrow(lmod$fr), ndim = length(s)) mkMerMod(environment(devfun), opt, lmod$reTrms, fr = lmod$fr, mc = mcout, lme4conv=cc) ## prepare output }## { lmer } ##' Fit a generalized linear mixed model (GLMM) glmer <- function(formula, data=NULL , family = gaussian , control = glmerControl() , start = NULL , verbose = 0L , nAGQ = 1L , subset, weights, na.action, offset, contrasts = NULL , mustart, etastart , devFunOnly = FALSE) { if (!inherits(control, "glmerControl")) { if(!is.list(control)) stop("'control' is not a list; use glmerControl()") ## back-compatibility kluge if (class(control)[1]=="lmerControl") { warning("please use glmerControl() instead of lmerControl()", immediate.=TRUE) control <- ## unpack sub-lists c(control[!names(control) %in% c("checkConv","checkControl")], control$checkControl,control$checkConv) control["restart_edge"] <- NULL ## not implemented for glmer } else { msg <- "Use control=glmerControl(..) instead of passing a list" if(length(cl <- class(control))) { msg <- paste(msg, "of class", dQuote(cl[1])) } warning(msg, immediate.=TRUE) } control <- do.call(glmerControl, control) } mc <- mcout <- match.call() ## family-checking code duplicated here and in glFormula (for now) since ## we really need to redirect at this point; eventually deprecate formally ## and clean up if (is.character(family)) family <- get(family, mode = "function", envir = parent.frame(2)) if( is.function(family)) family <- family() if (isTRUE(all.equal(family, gaussian()))) { ## redirect to lmer (with warning) warning("calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is deprecated;", " please call lmer() directly") mc[[1]] <- quote(lme4::lmer) mc["family"] <- NULL # to avoid an infinite loop return(eval(mc, parent.frame())) } ## see https://github.com/lme4/lme4/issues/50 ## parse the formula and data mc[[1]] <- quote(lme4::glFormula) glmod <- eval(mc, parent.frame(1L)) mcout$formula <- glmod$formula glmod$formula <- NULL if (is.matrix(y <- model.response(glmod$fr)) && ((family$family != "binomial" && ncol(y) > 1) || (ncol(y) >2))) { stop("can't handle matrix-valued responses: consider using refit()") } calc.derivs <- control$calc.derivs %||% (nrow(glmod$fr) < control$checkConv$check.conv.nobsmax) ## create deviance function for covariance parameters (theta) nAGQinit <- if(control$nAGQ0initStep) 0L else 1L devfun <- do.call(mkGlmerDevfun, c(glmod, list(verbose = verbose, control = control, nAGQ = nAGQinit))) if (nAGQ==0 && devFunOnly) return(devfun) pp <- environment(devfun)$pp ppdim <- length(pp$theta) + length(pp$delb) ## optimize deviance function over covariance parameters ## FIXME: perhaps should be in glFormula instead?? if (is.list(start)) { start.bad <- setdiff(names(start), c("theta","fixef", "beta")) if (length(start.bad)>0) { stop(sprintf("bad name(s) for start vector (%s); should be from {%s, %s, %s}", paste(start.bad,collapse=", "), shQuote("theta"), shQuote("fixef"), shQuote("beta")), call.=FALSE) } ## rename beta -> fixef internally if ("beta" %in% names(start)) { names(start)[names(start) == "beta"] <- "fixef" } if (!is.null(start$fixef) && nAGQ==0) { stop("should not specify both start$fixef (or $beta) and nAGQ==0") } } ## FIX ME: allow calc.derivs, use.last.params etc. if nAGQ=0 if(control$nAGQ0initStep) { opt <- optimizeGlmer(devfun, optimizer = control$optimizer[[1]], ## DON'T try fancy edge tricks unless nAGQ=0 explicitly set restart_edge=if (nAGQ==0) control$restart_edge else FALSE, boundary.tol=if (nAGQ==0) control$boundary.tol else 0, control = control$optCtrl, start=start, nAGQ = 0, verbose=verbose, calc.derivs=FALSE) } ## Note to self: length(opt) works for the theta parameters... if(nAGQ > 0L) { ## update deviance function to include fixed effects as inputs devfun <- updateGlmerDevfun(devfun, glmod$reTrms, nAGQ = nAGQ) if (control$nAGQ0initStep) { start <- updateStart(start,theta=opt$par) } ## if nAGQ0 was skipped ## we don't actually need to do anything here, it seems -- ## getStart gets called again in optimizeGlmer if (devFunOnly) return(devfun) ## reoptimize deviance function over covariance parameters and fixed effects opt <- optimizeGlmer(devfun, optimizer = control$optimizer[[2]], restart_edge=control$restart_edge, boundary.tol=control$boundary.tol, control = control$optCtrl, start=start, nAGQ=nAGQ, verbose = verbose, stage=2, calc.derivs=calc.derivs, use.last.params=control$use.last.params) } cc <- if (!calc.derivs) NULL else { if (verbose > 10) cat("checking convergence\n") checkConv(attr(opt,"derivs"),opt$par, ctrl = control$checkConv, lbound=environment(devfun)$lower, nobs = nrow(glmod$fr), ndim = ppdim) } ## prepare output mkMerMod(environment(devfun), opt, glmod$reTrms, fr = glmod$fr, mc = mcout, lme4conv=cc) }## {glmer} ##' Fit a nonlinear mixed-effects model nlmer <- function(formula, data=NULL, control = nlmerControl(), start = NULL, verbose = 0L, nAGQ = 1L, subset, weights, na.action, offset, contrasts = NULL, devFunOnly = FALSE) { vals <- nlformula(mc <- match.call()) p <- ncol(X <- vals$X) if ((rankX <- rankMatrix(X)) < p) stop(gettextf("rank of X = %d < ncol(X) = %d", rankX, p)) rho <- list2env(list(verbose=verbose, tolPwrss=0.001, # this is reset to the tolPwrss argument's value later resp=vals$resp, lower=vals$reTrms$lower), parent=parent.frame()) rho$pp <- do.call(merPredD$new, c(vals$reTrms[c("Zt","theta","Lambdat","Lind")], list(X=X, n=length(vals$respMod$mu), Xwts=vals$respMod$sqrtXwt, beta0=qr.coef(qr(X), unlist(lapply(vals$pnames, get, envir = rho$resp$nlenv)))))) rho$u0 <- rho$pp$u0 rho$beta0 <- rho$pp$beta0 ## deviance as a function of theta only : devfun <- mkdevfun(rho, 0L, verbose=verbose, control=control) if (devFunOnly && !nAGQ) return(devfun) devfun(rho$pp$theta) # initial coarse evaluation to get u0 and beta0 rho$u0 <- rho$pp$u0 rho$beta0 <- rho$pp$beta0 rho$tolPwrss <- control$tolPwrss # Reset control parameter (the initial optimization is coarse) ## set lower and upper bounds: if user-specified, select ## only the ones corresponding to random effects if (!is.null(lwr <- control$optCtrl$lower)) { rho$lower <- lwr[seq_along(rho$lower)] control$optCtrl$lower <- NULL } upper <- rep(Inf, length(rho$lower)) if (!is.null(upr <- control$optCtrl$upper)) { upper <- upr[seq_along(rho$lower)] control$optCtrl$upper <- NULL } opt <- optwrap(control$optimizer[[1]], devfun, rho$pp$theta, lower=rho$lower, upper=upper, control=control$optCtrl, adj=FALSE) rho$control <- attr(opt,"control") if (nAGQ > 0L) { ## set lower/upper to values already harvested from control$optCtrl$upper rho$lower <- if(!is.null(lwr)) lwr else c(rho$lower, rep.int(-Inf, length(rho$beta0))) upper <- if(!is.null(upr)) upr else c( upper, rep.int( Inf, length(rho$beta0))) rho$u0 <- rho$pp$u0 rho$dpars <- seq_along(rho$pp$theta) ## fixed-effect parameters rho$beta0 <- pmin(upper[-rho$dpars], pmax(rho$pp$beta0,rho$lower[-rho$dpars])) if (nAGQ > 1L) { if (length(vals$reTrms$flist) != 1L || length(vals$reTrms$cnms[[1]]) != 1L) stop("nAGQ > 1 is only available for models with a single, scalar random-effects term") rho$fac <- vals$reTrms$flist[[1]] } devfun <- mkdevfun(rho, nAGQ, verbose=verbose, control=control) if (devFunOnly) return(devfun) opt <- optwrap(control$optimizer[[2]], devfun, par = c(rho$pp$theta, rho$beta0), lower = rho$lower, upper = upper, control = control$optCtrl, adj = TRUE, verbose=verbose) } mkMerMod(environment(devfun), opt, vals$reTrms, fr = vals$frame, mc = mc) }## {nlmer} ## R 3.1.0 devel [2013-08-05]: This does not help yet if(getRversion() >= "3.1.0") utils::suppressForeignCheck("nlmerAGQ") if(getRversion() < "3.1.0") dontCheck <- identity ## *not* exported (had help page till early 2018) ## -> issue #92: -> also look at devfun2() in ./profile.R (which returns class!) ##' Create a deviance evaluation function from a predictor and a response module ##' @param rho an `environment` already containing `verbose` and tolPwrss ##' @param nAGQ for glmer/nlmer: #{AGQ steps}; 0 <==> Laplace ##' @param maxit maximal number of PIRLS iterations ##' @param verbose integer specifying if outputs should be produced ##' @param control a list as from lmerControl() etc mkdevfun <- function(rho, nAGQ=1L, maxit = if(extends(rho.cld, "nlsResp")) 300L else 100L, verbose=0, control=list()) { ## FIXME: should nAGQ be automatically embedded in rho? stopifnot(is.environment(rho), ## class definition, compute and save : extends(rho.cld <- getClass(class(rho$resp)), "lmResp")) ## silence R CMD check warnings *locally* in this function ## (clearly preferred to using globalVariables() !] fac <- pp <- resp <- lp0 <- compDev <- dpars <- baseOffset <- tolPwrss <- pwrssUpdate <- ## <-- even though it's a function below GQmat <- nlmerAGQ <- NULL ## The deviance function (to be returned, with 'rho' as its environment): ff <- if (extends(rho.cld, "lmerResp")) { rho$lmer_Deviance <- lmer_Deviance function(theta) .Call(lmer_Deviance, pp$ptr(), resp$ptr(), as.double(theta)) } else if (extends(rho.cld, "glmResp")) { ## control values will override rho values *if present* if (!is.null(tp <- control$tolPwrss)) rho$tolPwrss <- tp if (!is.null(cd <- control$ compDev)) rho$compDev <- cd if (nAGQ == 0L) function(theta) { resp$updateMu(lp0) pp$setTheta(theta) p <- pwrssUpdate(pp, resp, tol=tolPwrss, GQmat=GHrule(0L), compDev=compDev, maxit=maxit, verbose=verbose) resp$updateWts() p } else ## nAGQ > 0 function(pars) { ## pp$setDelu(rep(0, length(pp$delu))) resp$setOffset(baseOffset) resp$updateMu(lp0) pp$setTheta(as.double(pars[dpars])) # theta is first part of pars spars <- as.numeric(pars[-dpars]) offset <- if (length(spars)==0) baseOffset else baseOffset + pp$X %*% spars resp$setOffset(offset) p <- pwrssUpdate(pp, resp, tol=tolPwrss, GQmat=GQmat, compDev=compDev, grpFac=fac, maxit=maxit, verbose=verbose) resp$updateWts() p } } else if (extends(rho.cld, "nlsResp")) { if (nAGQ <= 1L) { rho$nlmerLaplace <- nlmerLaplace rho$tolPwrss <- control$tolPwrss rho$maxit <- maxit switch(nAGQ + 1L, function(theta) .Call(nlmerLaplace, pp$ptr(), resp$ptr(), as.double(theta), as.double(u0), beta0, verbose, FALSE, tolPwrss, maxit), function(pars) .Call(nlmerLaplace, pp$ptr(), resp$ptr(), pars[dpars], u0, pars[-dpars], verbose, TRUE, tolPwrss, maxit)) } else { stop("nAGQ > 1 not yet implemented for nlmer models") rho$nlmerAGQ <- nlmerAGQ rho$GQmat <- GHrule(nAGQ) ## function(pars) { ## .Call(nlmerAGQ, ## <- dontCheck(nlmerAGQ) should work according to docs but does not ## pp$ptr(), resp$ptr(), fac, GQmat, pars[dpars], ## u0, pars[-dpars], tolPwrss) ##} } } else stop("code not yet written") environment(ff) <- rho ff } ## Determine a step factor that will reduce the pwrss ## ## The penalized, weighted residual sum of squares (pwrss) is the sum ## of the weighted residual sum of squares from the resp module and ## the squared length of u from the predictor module. The predictor module ## contains a base value and an increment for the coefficients. ## @title Determine a step factor ## @param pp predictor module ## @param resp response module ## @param verbose logical value determining verbose output ## @return NULL if successful ## @note Typically all this is done in the C++ code. ## The R code is for debugging and comparisons of ## results. ## stepFac <- function(pp, resp, verbose, maxSteps = 10) { ## stopifnot(is.numeric(maxSteps), maxSteps >= 2) ## pwrss0 <- resp$wrss() + pp$sqrL(0) ## for (fac in 2^(-(0:maxSteps))) { ## wrss <- resp$updateMu(pp$linPred(fac)) ## pwrss1 <- wrss + pp$sqrL(fac) ## if (verbose > 3L) ## cat(sprintf("pwrss0=%10g, diff=%10g, fac=%6.4f\n", ## pwrss0, pwrss0 - pwrss1, fac)) ## if (pwrss1 <= pwrss0) { ## pp$installPars(fac) ## return(NULL) ## } ## } ## stop("step factor reduced below ",signif(2^(-maxSteps),2)," without reducing pwrss") ## } RglmerWrkIter <- function(pp, resp, uOnly=FALSE) { pp$updateXwts(resp$sqrtWrkWt()) pp$updateDecomp() pp$updateRes(resp$wtWrkResp()) if (uOnly) pp$solveU() else pp$solve() resp$updateMu(pp$linPred(1)) # full increment resp$resDev() + pp$sqrL(1) } ##' @param pp pred module ##' @param resp resp module ##' @param tol numeric tolerance ##' @param GQmat matrix of Gauss-Hermite quad info ##' @param compDev compute in C++ (as opposed to doing as much as possible in R) ##' @param grpFac grouping factor (normally found in environment ..) ##' @param verbose verbosity, of course glmerPwrssUpdate <- function(pp, resp, tol, GQmat, compDev=TRUE, grpFac=NULL, maxit = 70L, verbose=0) { nAGQ <- nrow(GQmat) if (compDev) { if (nAGQ < 2L) return(.Call(glmerLaplace, pp$ptr(), resp$ptr(), nAGQ, tol, as.integer(maxit), verbose)) return(.Call(glmerAGQ, pp$ptr(), resp$ptr(), tol, as.integer(maxit), GQmat, grpFac, verbose)) } ### does this show anywhere ??? [i.e. is it ever used in our checks/examples/scripts/vignettes ? ### message("glmerPwrssUpdate(*, compDev=FALSE) --> using more R, no direct .Call() to C.") # [DBG] only oldpdev <- .Machine$double.xmax uOnly <- nAGQ == 0L i <- 0 repeat { ## oldu <- pp$delu ## olddelb <- pp$delb pdev <- RglmerWrkIter(pp, resp, uOnly=uOnly) if (verbose > 2) cat(i,": ",pdev,"\n",sep="") ## check convergence first so small increases don't trigger errors if (is.na(pdev)) stop("encountered NA in PWRSS update") if (abs((oldpdev - pdev) / pdev) < tol) break ## if (pdev > oldpdev) { ## ## try step-halving ## ## browser() ## k <- 0 ## while (k < 10 && pdev > oldpdev) { ## pp$setDelu((oldu + pp$delu)/2.) ## if (!uOnly) pp$setDelb((olddelb + pp$delb)/2.) ## pdev <- RglmerWrkIter(pp, resp, uOnly=uOnly) ## k <- k+1 ## } ## } if (pdev > oldpdev) stop("PIRLS update failed") oldpdev <- pdev i <- i+1 } resp$Laplace(pp$ldL2(), 0., pp$sqrL(1)) ## FIXME: should 0. be pp$ldRX2 ? } ## create a deviance evaluation function that uses the sigma parameters ## df2 <- function(dd) { ## stopifnot(is.function(dd), ## length(formals(dd)) == 1L, ## is((rem <- (rho <- environment(dd))$rem), "Rcpp_reModule"), ## is((fem <- rho$fem), "Rcpp_deFeMod"), ## is((resp <- rho$resp), "Rcpp_lmerResp"), ## all((lower <- rem$lower) == 0)) ## Lind <- rem$Lind ## n <- length(resp$y) ## function(pars) { ## sigma <- pars[1] ## sigsq <- sigma * sigma ## sigmas <- pars[-1] ## theta <- sigmas/sigma ## rem$theta <- theta ## resp$updateMu(numeric(n)) ## solveBetaU(rem, fem, resp$sqrtXwt, resp$wtres) ## resp$updateMu(rem$linPred1(1) + fem$linPred1(1)) ## n * log(2*pi*sigsq) + (resp$wrss + rem$sqrLenU)/sigsq + rem$ldL2 ## } ## } ## bootMer() ---> now in ./bootMer.R ## Methods for the merMod class ## Anova for merMod objects ## ## @title anova() for merMod objects ## @param a merMod object ## @param ... further such objects ## @param refit should objects be refitted with ML (if applicable) ## @return an "anova" data frame; the traditional (S3) result of anova() anovaLmer <- function(object, ..., refit = TRUE, model.names=NULL) { mCall <- match.call(expand.dots = TRUE) dots <- list(...) .sapply <- function(L, FUN, ...) unlist(lapply(L, FUN, ...)) modp <- (as.logical(vapply(dots, is, NA, "merMod")) | as.logical(vapply(dots, is, NA, "lm"))) if (any(modp)) { ## multiple models - form table ## opts <- dots[!modp] mods <- c(list(object), dots[modp]) nobs.vec <- vapply(mods, nobs, 1L) if (var(nobs.vec) > 0) stop("models were not all fitted to the same size of dataset") ## model names if (is.null(mNms <- model.names)) mNms <- vapply(as.list(mCall)[c(FALSE, TRUE, modp)], deparse1, "") ## HACK to try to identify model names in situations such as ## 'do.call(anova,list(model1,model2))' where the model names ## are lost in the call stack ... this doesn't quite work but might ## be useful for future attempts? ## maxdepth <- -2 ## depth <- -1 ## while (depth >= maxdepth & ## all(grepl("S4 object of class structure",mNms))) { ## xCall <- match.call(call=sys.call(depth)) ## mNms <- .sapply(as.list(xCall)[c(FALSE, TRUE, modp)], deparse) ## depth <- depth-1 ## } ## if (depth < maxdepth) { if (any(substr(mNms, 1,4) == "new(") || any(duplicated(mNms)) || ## <- only if S4 objects are *not* properly deparsed max(nchar(mNms)) > 200) { warning("failed to find model names, assigning generic names") mNms <- paste0("MODEL",seq_along(mNms)) } if (length(mNms) != length(mods)) stop("model names vector and model list have different lengths") names(mods) <- sub("@env$", '', mNms) # <- hack models.reml <- vapply(mods, function(x) is(x,"merMod") && isREML(x), NA) models.GHQ <- vapply(mods, function(x) is(x,"glmerMod") && getME(x,"devcomp")$dims["nAGQ"]>1 , NA) if (any(models.GHQ) && any(vapply(mods, function(x) is(x,"glm"), NA))) stop("GLMMs with nAGQ>1 have log-likelihoods incommensurate with glm() objects") if (refit) { ## message only if at least one models is REML: if (any(models.reml)) message("refitting model(s) with ML (instead of REML)") mods[models.reml] <- lapply(mods[models.reml], refitML) } else { ## check that models are consistent (all REML or all ML) if(any(models.reml) && any(!models.reml)) warning("some models fit with REML = TRUE, some not") } ## devs <- sapply(mods, deviance) llks <- lapply(mods, logLik) ## Order models by increasing degrees of freedom: ii <- order(npar <- vapply(llks, attr, FUN.VALUE=numeric(1), "df")) mods <- mods[ii] llks <- llks[ii] npar <- npar [ii] calls <- lapply(mods, getCall) data <- lapply(calls, `[[`, "data") if(!all(vapply(data, identical, NA, data[[1]]))) stop("all models must be fit to the same data object") header <- paste("Data:", abbrDeparse(data[[1]])) subset <- lapply(calls, `[[`, "subset") if(!all(vapply(subset, identical, NA, subset[[1]]))) stop("all models must use the same subset") if (!is.null(subset[[1]])) header <- c(header, paste("Subset:", abbrDeparse(subset[[1]]))) llk <- unlist(llks) chisq <- 2 * pmax(0, c(NA, diff(llk))) dfChisq <- c(NA, diff(npar)) val <- data.frame(npar = npar, ## afraid to swap in vapply here; wondering ## why .sapply was needed in the first place ... AIC = .sapply(llks, AIC), # FIXME? vapply() BIC = .sapply(llks, BIC), # " " logLik = llk, "-2*log(L)" = -2*llk, Chisq = chisq, Df = dfChisq, "Pr(>Chisq)" = ifelse(dfChisq==0,NA,pchisq(chisq, dfChisq, lower.tail = FALSE)), row.names = names(mods), check.names = FALSE) class(val) <- c("anova", class(val)) forms <- lapply(lapply(calls, `[[`, "formula"), deparse1) structure(val, heading = c(header, "Models:", paste(rep.int(names(mods), lengths(forms)), unlist(forms), sep = ": "))) } else { ## ------ single model --------------------- if (length(dots)>0) { warnmsg <- "additional arguments ignored" nd <- names(dots) nd <- nd[nzchar(nd)] if (length(nd)>0) { warnmsg <- paste0(warnmsg,": ", paste(sQuote(nd),collapse=", ")) } warning(warnmsg) } dc <- getME(object, "devcomp") X <- getME(object, "X") stopifnot(length(asgn <- attr(X, "assign")) == dc$dims[["p"]]) ss <- as.vector(object@pp$RX() %*% object@beta)^2 names(ss) <- colnames(X) terms <- terms(object) nmeffects <- attr(terms, "term.labels")[unique(asgn)] if ("(Intercept)" %in% names(ss)) nmeffects <- c("(Intercept)", nmeffects) ss <- unlist(lapply(split(ss, asgn), sum)) stopifnot(length(ss) == length(nmeffects)) df <- lengths(split(asgn, asgn)) ## dfr <- unlist(lapply(split(dfr, asgn), function(x) x[1])) ms <- ss/df f <- ms/(sigma(object)^2) ## No longer provide p-values, but still the F statistic (may not be F distributed): ## ## P <- pf(f, df, dfr, lower.tail = FALSE) ## table <- data.frame(df, ss, ms, dfr, f, P) table <- data.frame(df, ss, ms, f) dimnames(table) <- list(nmeffects, ## c("npar", "Sum Sq", "Mean Sq", "Denom", "F value", "Pr(>F)")) c("npar", "Sum Sq", "Mean Sq", "F value")) if ("(Intercept)" %in% nmeffects) table <- table[-match("(Intercept)", nmeffects), ] structure(table, heading = "Analysis of Variance Table", class = c("anova", "data.frame")) } }## {anovaLmer} ##' @importFrom stats anova ##' @S3method anova merMod anova.merMod <- anovaLmer ##' @S3method as.function merMod as.function.merMod <- function(x, ...) { rho <- list2env(list(resp = x@resp$copy(), pp = x@pp$copy(), beta0 = x@beta, u0 = x@u), parent=as.environment("package:lme4")) ## FIXME: extract verbose [, maxit] and control mkdevfun(rho, getME(x, "devcomp")$dims[["nAGQ"]], ...) } ## coef() method for all kinds of "mer", "*merMod", ... objects ## ------ should work with fixef() + ranef() alone coefMer <- function(object, ...) { if(...length()) warning('arguments named ', paste(sQuote(...names()), collapse = ", "), ' ignored') fef <- data.frame(rbind(fixef(object)), check.names = FALSE) ref <- ranef(object, condVar = FALSE) ## check for variables in RE but missing from FE, fill in zeros in FE accordingly refnames <- unlist(lapply(ref,colnames)) nmiss <- length(missnames <- setdiff(refnames,names(fef))) if (nmiss > 0) { fillvars <- setNames(data.frame(rbind(rep(0,nmiss))),missnames) fef <- cbind(fillvars,fef) } val <- lapply(ref, function(x) fef[rep.int(1L, nrow(x)),,drop = FALSE]) for (i in seq_along(val)) { refi <- ref[[i]] row.names(val[[i]]) <- row.names(refi) nmsi <- colnames(refi) if (!all(nmsi %in% names(fef))) stop("unable to align random and fixed effects") for (nm in nmsi) val[[i]][[nm]] <- val[[i]][[nm]] + refi[,nm] } class(val) <- "coef.mer" val } ## {coefMer} ##' @importFrom stats coef ##' @S3method coef merMod coef.merMod <- coefMer ## FIXME: should these values (i.e. ML criterion for REML models ## and vice versa) be computed and stored in the object in the first place? ##' @importFrom stats deviance ##' @S3method deviance merMod deviance.merMod <- function(object, REML = NULL, ...) { ## type = c("conditional", "unconditional", "penalized"), ## relative = TRUE, ...) { if (isGLMM(object)) { return(sum(residuals(object,type="deviance")^2)) ## ------------------------------------------------------------ ## proposed change to deviance function for GLMMs ## ------------------------------------------------------------ ## @param type Type of deviance (can be unconditional, ## penalized, conditional) ## @param relative Should deviance be shifted relative to a ## saturated model? (only available with type == penalized or ## conditional) ## ------------------------------------------------------------ ## ans <- switch(type[1], ## unconditional = { ## if (relative) { ## stop("unconditional and relative deviance is undefined") ## } ## c(-2 * logLik(object)) ## }, ## penalized = { ## sqrL <- object@pp$sqrL(1) ## if (relative) { ## object@resp$resDev() + sqrL ## } else { ## useSc <- unname(getME(gm1, "devcomp")$dims["useSc"]) ## qLog2Pi <- unname(getME(object, "q")) * log(2 * pi) ## object@resp$aic() - (2 * useSc) + sqrL + qLog2Pi ## } ## }, ## conditional = { ## if (relative) { ## object@resp$resDev() ## } else { ## useSc <- unname(getME(gm1, "devcomp")$dims["useSc"]) ## object@resp$aic() - (2 * useSc) ## } ## }) ## return(ans) } if (isREML(object) && is.null(REML)) { warning("deviance() is deprecated for REML fits; use REMLcrit for the REML criterion or deviance(.,REML=FALSE) for deviance calculated at the REML fit") return(devCrit(object, REML=TRUE)) } devCrit(object, REML=FALSE) } REMLcrit <- function(object) { devCrit(object, REML=TRUE) } ## original deviance.merMod -- now wrapped by REMLcrit ## REML=NULL: ## if REML fit return REML criterion ## if ML fit, return deviance ## REML=TRUE: ## if not LMM, stop. ## if ML fit, compute and return REML criterion ## if REML fit, return REML criterion ## REML=FALSE: ## if ML fit, return deviance ## if REML fit, compute and return deviance devCrit <- function(object, REML = NULL) { ## cf. (1) lmerResp::Laplace in respModule.cpp ## (2) section 5.6 of lMMwR, listing lines 34-42 if (isTRUE(REML) && !isLMM(object)) stop("can't compute REML deviance for a non-LMM") cmp <- object@devcomp$cmp if (is.null(REML) || is.na(REML[1])) REML <- isREML(object) if (REML) { if (isREML(object)) { cmp[["REML"]] } else { ## adjust ML results to REML lnum <- log(2*pi*cmp[["pwrss"]]) n <- object@devcomp$dims[["n"]] nmp <- n - length(object@beta) ldW <- sum(log(weights(object, method = "prior"))) - ldW + cmp[["ldL2"]] + cmp[["ldRX2"]] + nmp*(1 + lnum - log(nmp)) } } else { if (!isREML(object)) { cmp[["dev"]] } else { ## adjust REML results to ML n <- object@devcomp$dims[["n"]] lnum <- log(2*pi*cmp[["pwrss"]]) ldW <- sum(log(weights(object, method = "prior"))) - ldW + cmp[["ldL2"]] + n*(1 + lnum - log(n)) } } } ## copied from stats:::safe_pchisq safe_pchisq <- function (q, df, ...) { df[df <= 0] <- NA pchisq(q = q, df = df, ...) } ##' @importFrom stats drop1 ##' @S3method drop1 merMod drop1.merMod <- function(object, scope, scale = 0, test = c("none", "Chisq", "user"), k = 2, trace = FALSE, sumFun=NULL, ...) { evalhack <- "formulaenv" test <- match.arg(test) if ((test=="user" && is.null(sumFun)) || ((test!="user" && !is.null(sumFun)))) stop(sQuote("sumFun"),' must be specified if (and only if) test=="user"') tl <- attr(terms(object), "term.labels") if(missing(scope)) scope <- drop.scope(object) else { if(!is.character(scope)) { scope <- attr(terms(getFixedFormula(update.formula(object, scope))), "term.labels") } if(!all(match(scope, tl, 0L) > 0L)) stop("scope is not a subset of term labels") } ns <- length(scope) if (is.null(sumFun)) { sumFun <- function(x,scale,k,...) setNames(extractAIC(x,scale,k,...),c("df","AIC")) } ss <- sumFun(object, scale=scale, k=k, ...) ans <- matrix(nrow = ns + 1L, ncol = length(ss), dimnames = list(c("", scope), names(ss))) ans[1, ] <- ss n0 <- nobs(object, use.fallback = TRUE) env <- environment(formula(object)) # perhaps here is where trouble begins?? for(i in seq_along(scope)) { ## was seq(ns), failed on empty scope tt <- scope[i] if(trace > 1) { cat("trying -", tt, "\n", sep='') flush.console() } ## FIXME: make this more robust, somehow? ## three choices explored so far: ## (1) evaluate nfit in parent frame: tests in inst/tests/test-formulaEval.R ## will fail on lapply(m_data_List,drop1) ## (formula environment contains r,x,y,z but not d) ## (2) evaluate nfit in frame of formula: tests will fail when data specified and formula is character ## (3) update with data=NULL: fails when ... ## if (evalhack %in% c("parent","formulaenv")) { nfit <- update(object, as.formula(paste("~ . -", tt)), evaluate = FALSE) ## nfit <- eval(nfit, envir = env) # was eval.parent(nfit) if (evalhack=="parent") { nfit <- eval.parent(nfit) } else if (evalhack=="formulaenv") { nfit <- eval(nfit,envir=env) } } else { nfit <- update(object, as.formula(paste("~ . -", tt)),data=NULL, evaluate = FALSE) nfit <- eval(nfit,envir=env) } if (test=="user") { ans[i+1, ] <- sumFun(object, nfit, scale=scale, k=k, ...) } else { ans[i+1, ] <- sumFun(nfit, scale, k = k, ...) } nnew <- nobs(nfit, use.fallback = TRUE) if(all(is.finite(c(n0, nnew))) && nnew != n0) stop("number of rows in use has changed: remove missing values?") } if (test=="user") { aod <- as.data.frame(ans) } else { dfs <- ans[1L, 1L] - ans[, 1L] dfs[1L] <- NA aod <- data.frame(npar = dfs, AIC = ans[,2]) if(test == "Chisq") { ## reconstruct deviance from AIC (ugh) dev <- ans[, 2L] - k*ans[, 1L] dev <- dev - dev[1L] ; dev[1L] <- NA nas <- !is.na(dev) P <- dev P[nas] <- safe_pchisq(dev[nas], dfs[nas], lower.tail = FALSE) aod[, c("LRT", "Pr(Chi)")] <- list(dev, P) } else if (test == "F") { ## FIXME: allow this if denominator df are specified externally? stop("F test STUB -- unfinished maybe forever") dev <- ans[, 2L] - k*ans[, 1L] dev <- dev - dev[1L] ; dev[1L] <- NA nas <- !is.na(dev) P <- dev P[nas] <- safe_pchisq(dev[nas], dfs[nas], lower.tail = FALSE) aod[, c("LRT", "Pr(F)")] <- list(dev, P) } } head <- c("Single term deletions", "\nModel:", deparse(formula(object)), if(scale > 0) paste("\nscale: ", format(scale), "\n")) if (!is.null(method <- attr(ss,"method"))) { head <- c(head,"Method: ",method,"\n") } structure(aod, heading = head, class = c("anova", "data.frame")) } ##' @importFrom stats extractAIC ##' @S3method extractAIC merMod extractAIC.merMod <- function(fit, scale = 0, k = 2, ...) { L <- logLik(refitML(fit)) edf <- attr(L,"df") c(edf,-2*L + k*edf) } ##' @importFrom stats family ##' @S3method family merMod family.merMod <- function(object, ...) family(object@resp, ...) ##' @S3method family glmResp family.glmResp <- function(object, ...) { # regenerate initialize # expression if necessary ## FIXME: may fail with user-specified/custom family? ## should be obsolete if(is.null(object$family$initialize)) return(do.call(object$family$family, list(link=object$family$link))) object$family } ##' @S3method family lmResp family.lmResp <- function(object, ...) gaussian() ##' @S3method family nlsResp family.nlsResp <- function(object, ...) gaussian() ##' @importFrom stats fitted ##' @S3method fitted merMod fitted.merMod <- function(object, ...) { xx <- object@resp$mu if (length(xx)==0) { ## handle 'fake' objects created by simulate() xx <- rep(NA,nrow(model.frame(object))) } if (is.null(nm <- rownames(model.frame(object)))) nm <- seq_along(xx) names(xx) <- nm if (!is.null(fit.na.action <- attr(model.frame(object),"na.action"))) napredict(fit.na.action, xx) else xx } ##' Extract the fixed-effects estimates ##' ##' Extract the estimates of the fixed-effects parameters from a fitted model. ##' @name fixef ##' @title Extract fixed-effects estimates ##' @aliases fixef fixed.effects fixef.merMod ##' @docType methods ##' @param object any fitted model object from which fixed effects estimates can ##' be extracted. ##' @param noScale logical; if TRUE, returns the non-scaled parameters ##' @param \dots optional additional arguments. Currently none are used in any ##' methods. ##' @return a named, numeric vector of fixed-effects estimates. ##' @keywords models ##' @examples ##' fixef(lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) ##' @importFrom nlme fixef ##' @export fixef ##' @method fixef merMod ##' @export fixef.merMod <- function(object, add.dropped = FALSE, noScale = NULL, ...) { X <- getME(object,"X") ff <- structure(object@beta, names = dimnames(X)[[2]]) if(is.null(noScale) || (!is.null(noScale) && !noScale)){ if (!is.null(sc <- attr(X, "scaled:scale"))) { ce <- attr(X, "scaled:center") ## modifying intercept if ("(Intercept)" %in% names(ff)) { intercept_shift <- sum((ff[names(sc)] * ce[names(sc)]) / sc[names(sc)]) ff[["(Intercept)"]] <- ff[["(Intercept)"]] - intercept_shift } # modifying beta coefficients ff[names(sc)] <- ff[names(sc)] / sc[names(sc)] } } if (add.dropped) { if (!is.null(dd <- attr(X,"col.dropped"))) { ## restore positions dropped for rank deficiency vv <- numeric(length(ff)+length(dd)) all.pos <- seq_along(vv) kept.pos <- all.pos[-dd] vv[kept.pos] <- ff names(vv)[kept.pos] <- names(ff) vv[dd] <- NA names(vv)[dd] <- names(dd) ff <- vv } } return(ff) } getFixedFormula <- function(form) { RHSForm(form) <- reformulas::nobars(RHSForm(form)) form } ##' @importFrom stats formula ##' @S3method formula merMod formula.merMod <- function(x, fixed.only=FALSE, random.only=FALSE, ...) { if (missing(fixed.only) && random.only) fixed.only <- FALSE if (fixed.only && random.only) stop("can't specify 'only fixed' and 'only random' terms") if (is.null(form <- attr(x@frame,"formula"))) { if (!grepl("lmer$",deparse(getCall(x)[[1]]))) stop("can't find formula stored in model frame or call") form <- as.formula(formula(getCall(x),...)) } if (fixed.only) { form <- getFixedFormula(form) } if (random.only) { ## from predict.R form <- reOnly(form,response=TRUE) } form } ##' @S3method isREML merMod isREML.merMod <- function(x, ...) as.logical(x@devcomp$dims[["REML"]]) ##' @S3method isGLMM merMod isGLMM.merMod <- function(x,...) { as.logical(x@devcomp$dims[["GLMM"]]) ## or: is(x@resp,"glmResp") } ##' @S3method isNLMM merMod isNLMM.merMod <- function(x,...) { as.logical(x@devcomp$dims[["NLMM"]]) ## or: is(x@resp,"nlsResp") } ##' @S3method isLMM merMod isLMM.merMod <- function(x,...) { !isGLMM(x) && !isNLMM(x) ## or: is(x@resp,"lmerResp") ? } npar.merMod <- function(object) { n <- length(object@beta) + length(object@theta) + object@devcomp[["dims"]][["useSc"]] ## FIXME: this is a bit of a hack: a user *might* have specified ## negative binomial family with a known theta, in which case we ## shouldn't count it as extra. Either glmer.nb needs to set a ## flag somewhere, or we need class 'nbglmerMod' to extend 'glmerMod' ... ## We do *not* want to use the 'useSc' slot (as above), because ## although theta is in some sense a scale parameter, it's not ## one in the formal sense (and isn't stored in the 'sigma' slot) if (grepl("Negative Binomial",family(object)$family)) { n <- n+1 } return(n) ## TODO: how do we feel about counting the scale parameter ??? } ##' @importFrom stats logLik ##' @S3method logLik merMod logLik.merMod <- function(object, REML = NULL, ...) { if (is.null(REML) || is.na(REML[1])) REML <- isREML(object) val <- -devCrit(object, REML = REML)/2 ## dc <- object@devcomp nobs <- nobs.merMod(object) structure(val, nobs = nobs, nall = nobs, df = npar.merMod(object), ## length(object@beta) + length(object@theta) + dc$dims[["useSc"]], class = "logLik") } ##' @importFrom stats df.residual ##' @S3method df.residual merMod ## TODO: not clear whether the residual df should be based ## on p=length(beta) or p=length(c(theta,beta)) ... but ## this is just to allow things like aods3::gof to work ... ## df.residual.merMod <- function(object, ...) { nobs(object)-npar.merMod(object) } ##' @importFrom stats logLik ##' @S3method model.frame merMod model.frame.merMod <- function(formula, fixed.only=FALSE, ...) { fr <- formula@frame if (fixed.only) { vars <- attr(terms(fr),"varnames.fixed") if (is.null(vars)) { ## back-compatibility: saved objects pre 1.1-15 ff <- formula(formula,fixed.only=TRUE) ## thanks to Thomas Leeper and Roman Lustrik, Stack Overflow ## https://stackoverflow.com/questions/18017765/extract-variables-in-formula-from-a-data-frame vars <- rownames(attr(terms.formula(ff), "factors")) } vars <- gsub("`","",vars) ## weirdness in deparsing variable names with spaces fr <- fr[vars] } fr } ##' @importFrom stats model.matrix ##' @S3method model.matrix merMod ##' @param noScale logical; if TRUE, returns the non-scaled parameters model.matrix.merMod <- function(object, type = c("fixed", "random", "randomListRaw"), noScale = NULL,...) { X <- object@pp$X # Re-scales back the model matrix on command if (!is.null(sc <- attr(object@pp$X, "scaled:scale"))){ if((is.null(noScale)) || (!is.null(noScale) && !noScale)){ unscale_cols <- setdiff(colnames(X), "(Intercept)") ce <- attr(object@pp$X, "scaled:center") X[, unscale_cols] <- sweep(X[, unscale_cols], 2, sc, `*`) X[, unscale_cols] <- sweep(X[, unscale_cols], 2, ce, `+`) } } switch(type[1], "fixed" = X, "random" = getME(object, "Z"), "randomListRaw" = mmList(object)) } ##' Dummy variables (experimental) ##' ##' Largely a wrapper for \code{model.matrix} that ##' accepts a factor, \code{f}, and returns a dummy ##' matrix with \code{nlevels(f)-1} columns. dummy <- function(f, levelsToKeep){ f <- as.factor(f) if (all(is.na(f))) return(rep(NA_real_, length(f))) mm <- model.matrix(~ 0 + f) colnames(mm) <- levels(f) # sort out levels to keep missingLevels <- missing(levelsToKeep) if(missingLevels) levelsToKeep <- levels(f)[-1] if(!any(levels(f) %in% levelsToKeep)) stop("at least some of the levels in f ", "must also be present in levelsToKeep") if(!all(levelsToKeep %in% levels(f))) stop("all of the levelsToKeep must be levels of f") mm <- mm[, levelsToKeep, drop=FALSE] ## # communicate that some usages are unlikely ## # to help with readibility, which is the ## # whole purpose of dummy() ## if((!missingLevels)&&(ncol(mm) > 1)) ## message("note from dummy: explicitly specifying more than one ", ## "level to keep may do little to improve readibility") return(mm) } ##' @importFrom stats nobs ##' @S3method nobs merMod nobs.merMod <- function(object, ...) nrow(object@frame) ## used in summary.merMod(): ngrps <- function(object, ...) UseMethod("ngrps") ngrps.default <- function(object, ...) stop("Cannot extract the number of groups from this object") ngrps.merMod <- function(object, ...) vapply(object@flist, nlevels, 1) ngrps.factor <- function(object, ...) nlevels(object) ##' @importFrom nlme ranef ##' @export ranef NULL ##' Extract the modes of the random effects ##' ##' A generic function to extract the conditional modes of the random effects ##' from a fitted model object. For linear mixed models the conditional modes ##' of the random effects are also the conditional means. ##' ##' If grouping factor i has k levels and j random effects per level the ith ##' component of the list returned by \code{ranef} is a data frame with k rows ##' and j columns. If \code{condVar} is \code{TRUE} the \code{"postVar"} ##' attribute is an array of dimension j by j by k. The kth face of this array ##' is a positive definite symmetric j by j matrix. If there is only one ##' grouping factor in the model the variance-covariance matrix for the entire ##' random effects vector, conditional on the estimates of the model parameters ##' and on the data will be block diagonal and this j by j matrix is the kth ##' diagonal block. With multiple grouping factors the faces of the ##' \code{"postVar"} attributes are still the diagonal blocks of this ##' conditional variance-covariance matrix but the matrix itself is no longer ##' block diagonal. ##' @name ranef ##' @aliases ranef ranef.merMod ##' @param object an object of a class of fitted models with random effects, ##' typically an \code{"\linkS4class{merMod}"} object. ##' @param condVar an optional logical argument indicating if the conditional ##' variance-covariance matrices of the random effects should be added as an attribute. ##' @param postVar a (deprecated) synonym for \code{condVar} ##' @param drop an optional logical argument indicating components of the return ##' value that would be data frames with a single column, usually a column ##' called \sQuote{\code{(Intercept)}}, should be returned as named vectors. ##' @param whichel an optional character vector of names of grouping factors for ##' which the random effects should be returned. Defaults to all the grouping ##' factors. ##' @param \dots some methods for this generic function require additional ##' arguments. ##' @return A list of data frames, one for each grouping factor for the random ##' effects. The number of rows in the data frame is the number of levels of ##' the grouping factor. The number of columns is the dimension of the random ##' effect associated with each level of the factor. ##' ##' If \code{condVar} is \code{TRUE} each of the data frames has an attribute ##' called \code{"postVar"} which is a three-dimensional array with symmetric ##' faces. ##' ##' When \code{drop} is \code{TRUE} any components that would be data frames of ##' a single column are converted to named numeric vectors. ##' @note To produce a \dQuote{caterpillar plot} of the random effects apply ##' \code{\link[lattice:xyplot]{dotplot}} to the result of a call to ##' \code{ranef} with \code{condVar = TRUE}. ##' @examples ##' fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ##' fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy) ##' fm3 <- lmer(diameter ~ (1|plate) + (1|sample), Penicillin) ##' ranef(fm1) ##' str(rr1 <- ranef(fm1, condVar = TRUE)) ##' dotplot(rr1) ## default ##' ## specify free scales in order to make Day effects more visible ##' dotplot(rr1,scales = list(x = list(relation = 'free')))[["Subject"]] ##' str(ranef(fm2, condVar = TRUE)) ##' op <- options(digits = 4) ##' ranef(fm3, drop = TRUE) ##' options(op) ##' @keywords models methods ##' @method ranef merMod ##' @export ranef.merMod <- function(object, condVar = TRUE, drop = FALSE, whichel = names(ans), postVar = FALSE, ...) { if (length(L <- list(...))>0) { warning(paste("additional arguments to ranef.merMod ignored:", paste(names(L),collapse=", "))) } if (!missing(postVar) && missing(condVar)) { warning(sQuote("postVar")," is deprecated: please use ", sQuote("condVar")," instead") condVar <- postVar } ans <- object@pp$b(1) ## not always == c(matrix(unlist(getME(object,"b")))) if (!is.null(fl <- object@flist)) { ## evaluate the list of matrices levs <- lapply(fl, levels) asgn <- attr(fl, "assign") cnms <- object@cnms nc <- lengths(cnms) ## number of terms ## nb <- nc * lengths(levs)[asgn] ## number of cond modes per term nb <- diff(object@Gp) ## differencing group index is more robust nbseq <- rep.int(seq_along(nb), nb) ml <- split(ans, nbseq) for (i in seq_along(ml)) ml[[i]] <- matrix(ml[[i]], ncol = nc[i], byrow = TRUE, dimnames = list(NULL, cnms[[i]])) ## create a list of data frames corresponding to factors ans <- lapply(seq_along(fl), function(i) { m <- ml[asgn == i] b2 <- vapply(m,nrow,numeric(1)) ub2 <- unique(b2) if (length(ub2)>1) stop("differing numbers of b per group") ## if number of sets of modes != number of levels (e.g. Gaussian process/phyloglmm), ## generate numeric sequence for names rnms <- if (ub2==length(levs[[i]])) levs[[i]] else seq(ub2) data.frame(do.call(cbind, m), row.names = rnms, check.names = FALSE) }) names(ans) <- names(fl) # process whichel stopifnot(is(whichel, "character")) whchL <- names(ans) %in% whichel ans <- ans[whchL] if (condVar) { sigsqr <- sigma(object)^2 rp <- rePos$new(object) if(any(lengths(rp$terms) > 1L)) { ## use R machinery here ... vv <- arrange.condVar(object,condVar(object, scaled=TRUE)) } else { vv <- .Call(merPredDcondVar, object@pp$ptr(), as.environment(rp)) vv <- lapply(vv, "*", sigsqr) } for (i in names(ans)) { attr(ans[[i]], "postVar") <- vv[[i]] } } if (drop) ans <- lapply(ans, function(el) { if (ncol(el) > 1) return(el) pv <- drop(attr(el, "postVar")) el <- drop(as.matrix(el)) if (!is.null(pv)) attr(el, "postVar") <- pv el }) class(ans) <- "ranef.mer" } ans }## ranef.merMod print.ranef.mer <- function(x, ...) { print(unclass(x), ...) if(any(has.pv <- vapply(x, function(el) !is.null(attr(el, "postVar")), NA))) cat('with conditional variances for', paste(dQuote(names(x)[has.pv]), sep=", "), "\n") invisible(x) } ## try to redo refit by calling modular structure ... refit2.merMod <- function(object, newresp=NULL) { ## the idea is to steal as much structure as we can from the ## previous fit, including ## * starting parameter values ## * random-effects structure ## * fixed-effects structure ## * model frame ## and jump into the modular structure at an appropriate place; ## essentially, this should merge with a smart-as-possible ## version of 'update' ... } ## FIXME DRY: much of copy'n'paste from lmer() etc .. ==> become more modular (?) refit.merMod <- function(object, newresp = NULL, newweights = NULL, ## formula=NULL, weights=NULL, rename.response = FALSE, maxit = 100L, ...) { l... <- list(...) ctrl.arg <- NULL if("control" %in% names(l...)) ctrl.arg <- l...$control if(!all(names(l...) %in% c("control", "verbose"))) { warning("additional arguments to refit.merMod ignored") } ## TODO: not clear whether we should reset the names ## to the new response variable. Maybe not. ## retrieve name before it gets mangled by operations on newresp newrespSub <- substitute(newresp) ## for backward compatibility/functioning of refit(fit,simulate(fit)) if (is.list(newresp)) { if (length(newresp)==1) { na.action <- attr(newresp,"na.action") newresp <- newresp[[1]] attr(newresp,"na.action") <- na.action } else { stop("refit not implemented for 'newresp' lists of length > 1: ", "consider ", sQuote("lapply(object,refit)")) } } ## oldresp <- object@resp$y # need to set this before deep copy, ## # otherwise it gets reset with the call ## # to setResp below ## somewhat repeated from profile.merMod, but sufficiently ## different that refactoring is slightly non-trivial ## "three minutes' thought would suffice ..." control <- if (!is.null(ctrl.arg)) { if (length(ctrl.arg$optCtrl) == 0) { ## use object's version: obj.control <- object@optinfo$control ignore.pars <- c("xst", "xt") if (any(ign <- names(obj.control) %in% ignore.pars)) obj.control <- obj.control[!ign] ctrl.arg$optCtrl <- obj.control } ctrl.arg } else if (isGLMM(object)) glmerControl() else lmerControl() if (object@optinfo$optimizer == "optimx") { control$optCtrl <- object@optinfo$control } ## we need this stuff defined before we call .glmerLaplace below ... pp <- object@pp$copy() dc <- object@devcomp nAGQ <- dc$dims["nAGQ"] # possibly NA nth <- dc$dims[["nth"]] verbose <- l...$verbose; if (is.null(verbose)) verbose <- 0L if (!is.null(newresp)) { ## update call and model frame with new response rcol <- attr(attr(model.frame(object), "terms"), "response") if (rename.response) { attr(object@frame,"formula")[[2]] <- object@call$formula[[2]] <- newrespSub names(object@frame)[rcol] <- deparse(newrespSub) } if (!is.null(na.act <- attr(object@frame,"na.action")) && is.null(attr(newresp,"na.action"))) { ## will only get here if na.action is 'na.omit' or 'na.exclude' ## *and* newresp does not have an 'na.action' attribute ## indicating that NAs have already been filtered newresp <- if (is.matrix(newresp)) newresp[-na.act, ] else newresp[-na.act] } object@frame[[rcol]] <- newresp } if (!is.null(newweights)) { ## DRY ... if (!is.null(na.act <- attr(object@frame,"na.action")) && is.null(attr(newweights, "na.action"))) { newweights <- newweights[-na.act] } object@frame[["(weights)"]] <- newweights oc <- attr(attr(object@frame, "terms"), "dataClasses") attr(attr(object@frame, "terms"), "dataClasses") <- c(oc, `(weights)` = "numeric") object@call$weights <- substitute(newweights) ## try to make sure new weights are findable later assign(deparse(substitute(newweights)), newweights, environment(formula(object))) } rr <- if(isLMM(object)) mkRespMod(model.frame(object), REML = object@resp$REML) else if(isGLMM(object)) { mkRespMod(model.frame(object), family = family(object)) } else stop("refit.merMod not working for nonlinear mixed models.\n", "try update.merMod instead.") if(!is.null(newresp)) { if(family(object)$family == "binomial") { ## re-do conversion of two-column matrix and factor ## responses to proportion/weights format if (is.matrix(newresp) && ncol(newresp) == 2) { ntot <- rowSums(newresp) ## FIXME: test what happens for (0,0) rows newresp <- newresp[,1]/ntot rr$setWeights(ntot) } if (is.factor(newresp)) { ## FIXME: would be better to do this consistently with ## whatever machinery is used in glm/glm.fit/glmer ?? newresp <- as.numeric(newresp)-1 } } ## if (isGLMM(object) && rr$family$family=="binomial") { ## } stopifnot(length(newresp <- as.numeric(as.vector(newresp))) == length(rr$y)) } if (isGLMM(object)) { GQmat <- GHrule(nAGQ) if (nAGQ <= 1) { glmerPwrssUpdate(pp,rr, control$tolPwrss, GQmat, maxit=maxit) } else { glmerPwrssUpdate(pp,rr, control$tolPwrss, GQmat, maxit=maxit, grpFac = object@flist[[1]]) } } devlist <- if (isGLMM(object)) { baseOffset <- forceCopy(object@resp$offset) list(tolPwrss= dc$cmp [["tolPwrss"]], compDev = dc$dims[["compDev"]], nAGQ = unname(nAGQ), lp0 = pp$linPred(1), ## object@resp$eta - baseOffset, baseOffset = baseOffset, pwrssUpdate = glmerPwrssUpdate, ## save GQmat in the object and use that instead of nAGQ GQmat = GHrule(nAGQ), fac = object@flist[[1]], pp=pp, resp=rr, u0=pp$u0, verbose=verbose, dpars=seq_len(nth)) } else list(pp=pp, resp=rr, u0=pp$u0, verbose=verbose, dpars=seq_len(nth)) ff <- mkdevfun(list2env(devlist), nAGQ=nAGQ, maxit=maxit, verbose=verbose) ## rho <- environment(ff) == list2env(devlist) xst <- rep.int(0.1, nth) x0 <- pp$theta lower <- object@lower if (!is.na(nAGQ) && nAGQ > 0L) { xst <- c(xst, sqrt(diag(pp$unsc()))) x0 <- c(x0, unname(fixef(object))) lower <- c(lower, rep(-Inf,length(x0)-length(lower))) } ## control <- c(control,list(xst=0.2*xst, xt=xst*0.0001)) ## FIX ME: allow use.last.params to be passed through calc.derivs <- !is.null(object@optinfo$derivs) ## if(isGLMM(object)) { ## rho$resp$updateWts() ## rho$pp$updateDecomp() ## rho$lp0 <- rho$pp$linPred(1) ## } optimizer <- object@optinfo$optimizer if (!is.null(newopt <- ctrl.arg$optimizer)) { ## we might end up with a length-2 optimizer vector ... ## use the *last* element optimizer <- newopt[length(newopt)] } opt <- optwrap(optimizer, ff, x0, lower=lower, control=control$optCtrl, calc.derivs=calc.derivs) cc <- checkConv(attr(opt,"derivs"),opt$par, ## FIXME: was there a reason that ctrl was passed ## via the call slot? it was causing problems ## when optTheta called refit (github issue #173) # ctrl = eval(object@call$control)$checkConv, ctrl = control$checkConv, lbound=lower) if (isGLMM(object)) rr$setOffset(baseOffset) mkMerMod(environment(ff), opt, list(flist=object@flist, cnms=object@cnms, Gp=object@Gp, lower=object@lower), object@frame, getCall(object), cc) } refitML.merMod <- function (x, optimizer="bobyqa", ...) { ## FIXME: optimizer is set to 'bobyqa' for back-compatibility, but that's not ## consistent with lmer (default NM). Should be based on internally stored 'optimizer' value if (!isREML(x)) return(x) stopifnot(is(rr <- x@resp, "lmerResp")) rho <- new.env(parent=parent.env(environment())) rho$resp <- new(class(rr), y=rr$y, offset=rr$offset, weights=rr$weights, REML=0L) xpp <- x@pp$copy() rho$pp <- new(class(xpp), X=xpp$X, Zt=xpp$Zt, Lambdat=xpp$Lambdat, Lind=xpp$Lind, theta=xpp$theta, n=nrow(xpp$X)) devfun <- mkdevfun(rho, 0L) # FIXME? also pass {verbose, maxit, control} opt <- ## "smart" calc.derivs rules if(optimizer == "bobyqa" && !any("calc.derivs" == ...names())) optwrap(optimizer, devfun, x@theta, lower=x@lower, calc.derivs=TRUE, ...) else optwrap(optimizer, devfun, x@theta, lower=x@lower, ...) ## FIXME: Should be able to call mkMerMod() here, and be done n <- length(rr$y) pp <- rho$pp p <- ncol(pp$X) dims <- c(N=n, n=n, p=p, nmp=n-p, q=nrow(pp$Zt), nth=length(pp$theta), useSc=1L, reTrms=length(x@cnms), spFe=0L, REML=0L, GLMM=0L, NLMM=0L)#, nAGQ=NA_integer_) wrss <- rho$resp$wrss() ussq <- pp$sqrL(1) pwrss <- wrss + ussq cmp <- c(ldL2=pp$ldL2(), ldRX2=pp$ldRX2(), wrss=wrss, ussq=ussq, pwrss=pwrss, drsum=NA, dev=opt$fval, REML=NA, sigmaML=sqrt(pwrss/n), sigmaREML=sqrt(pwrss/(n-p))) ## modify the call to have REML=FALSE. (without evaluating the call!) cl <- x@call cl[["REML"]] <- FALSE new("lmerMod", call = cl, frame=x@frame, flist=x@flist, cnms=x@cnms, theta=pp$theta, beta=pp$delb, u=pp$delu, optinfo = .optinfo(opt), lower=x@lower, devcomp=list(cmp=cmp, dims=dims), pp=pp, resp=rho$resp, Gp=x@Gp) } ##' residuals of merMod objects --> ../man/residuals.merMod.Rd ##' @param object a fitted [g]lmer (\code{merMod}) object ##' @param type type of residuals ##' @param scaled scale residuals by residual standard deviation (=scale parameter)? ##' @param \dots additional arguments (ignored: for method compatibility) residuals.merMod <- function(object, type = if(isGLMM(object)) "deviance" else "response", scaled = FALSE, ...) { r <- residuals(object@resp, type,...) fr <- model.frame(object) if (is.null(nm <- rownames(fr))) nm <- seq_along(r) names(r) <- nm if (scaled) r <- r/sigma(object) if (!is.null(na.action <- attr(fr, "na.action"))) naresid(na.action, r) else r } ##' @rdname residuals.merMod ##' @S3method residuals lmResp ##' @method residuals lmResp residuals.lmResp <- function(object, type = c("working", "response", "deviance", "pearson", "partial"), ...) { y <- object$y r <- object$wtres mu <- object$mu switch(match.arg(type), working =, response = y-mu, deviance =, pearson = r, partial = stop(gettextf("partial residuals are not implemented yet"), call. = FALSE) ) } ##' @rdname residuals.merMod ##' @S3method residuals glmResp ##' @method residuals glmResp residuals.glmResp <- function(object, type = c("deviance", "pearson", "working", "response", "partial"), ...) { type <- match.arg(type) y <- object$y mu <- object$mu switch(type, deviance = { ## protect against slightly negative resids ## (GH 812) d.res <- sqrt(pmax(0,object$devResid())) ifelse(y > mu, d.res, -d.res) }, pearson = object$wtres, working = object$wrkResids(), response = y - mu, partial = stop(gettextf("partial residuals are not implemented yet"), call. = FALSE) ) } hatvalues.merMod <- function(model, fullHatMatrix = FALSE, ...) { if(isGLMM(model)) warning("the hat matrix may not make sense for GLMMs") ## FIXME: add restriction for NLMMs? ## prior weights, W ^ {1/2} : sqrtW <- Diagonal(x = sqrt(weights(model, type = "prior"))) vList <- getME(model, c("L", "Lambdat", "Zt", "RX", "X", "RZX")) ## CL:= right factor of the random-effects component of the hat matrix (64) CL <- with(vList, solve(L, solve(L, Lambdat %*% Zt %*% sqrtW, system = "P"), system = "L")) ## restore dimnames (needed since Matrix 1.5.2) dimnames(CL) <- dimnames(vList$Zt) ## CR:= right factor of the fixed-effects component of the hat matrix (65) ## {MM (FIXME Matrix): t(.) %*% here faster than crossprod()} CR <- with(vList, solve(t(RX), ## colScale(t(X), sqrtW) - crossprod(RZX, CL) t(X) %*% sqrtW - crossprod(RZX, CL)) ) res <- if(fullHatMatrix) { ## H = (C_L^T C_L + C_R^T C_R) (63) crossprod(CL) + crossprod(CR) } else { ## diagonal of the hat matrix, diag(H) : colSums(CR^2) + colSums(CL^2) } napredict(attr(model.frame(model),"na.action"), res) } ###----- Printing etc ---------------------------- ## lme4.0, for GLMM had ## 'Generalized linear mixed model fit by the Laplace approximation' ## 'Generalized linear mixed model fit by the adaptive Gaussian Hermite approximation' ## so did *not* mention "maximum likelihood" at all in the GLMM case methTitle <- function(dims) { # dims == object@devcomp$dims GLMM <- dims[["GLMM"]] kind <- switch(1L + GLMM * 2L + dims[["NLMM"]], "Linear", "Nonlinear", "Generalized linear", "Generalized nonlinear") paste(kind, "mixed model fit by", if(dims[["REML"]]) "REML" else paste("maximum likelihood", if(GLMM) { ## TODO? Use shorter wording here, for (new) 'long = FALSE' argument if((nAGQ <- dims[["nAGQ"]]) == 1) "(Laplace Approximation)" else sprintf("(Adaptive Gauss-Hermite Quadrature, nAGQ = %d)", nAGQ) })) } cat.f <- function(...) cat(..., fill = TRUE) famlink <- function(object, resp = object@resp) { if(is(resp, "glmResp")) resp$family[c("family", "link")] else list(family = NULL, link = NULL) } ##' @title print method title ##' @param mtit the result of methTitle(obj) ##' @param class typically class(obj) .prt.methTit <- function(mtit, class) { if(nchar(mtit) + 5 + nchar(class) > (w <- getOption("width"))) { ## wrap around mtit <- strwrap(mtit, width = w - 2, exdent = 2) cat(mtit, " [",class,"]", sep = "", fill = TRUE) } else ## previous: simple one-liner cat(sprintf("%s ['%s']\n", mtit, class)) } .prt.family <- function(famL) { if (!is.null(f <- famL$family)) { cat.f(" Family:", f, if(!is.null(ll <- famL$link)) paste(" (", ll, ")")) } } .prt.resids <- function(resids, digits, title = "Scaled residuals:", ...) { cat(title,"\n") ## FIXME: need testing code rq <- setNames(zapsmall(quantile(resids, na.rm=TRUE), digits + 1L), c("Min", "1Q", "Median", "3Q", "Max")) print(rq, digits = digits, ...) cat("\n") } .prt.call <- function(call, long = TRUE) { if (!is.null(cc <- call$formula)) cat.f("Formula:", deparse(cc)) if (!is.null(cc <- call$data)) cat.f(" Data:", deparse(cc)) if (!is.null(cc <- call$weights)) cat.f("Weights:", deparse(cc)) if (!is.null(cc <- call$offset)) cat.f(" Offset:", deparse(cc)) if (long && length(cc <- call$control) && !identical((dc <- deparse(cc)), "lmerControl()")) ## && !identical(eval(cc), lmerControl())) cat.f("Control:", dc) if (!is.null(cc <- call$subset)) cat.f(" Subset:", deparse(cc)) } ##' @title Extract Log Likelihood, AIC, and related statics from a Fitted LMM ##' @param object a LMM model fit ##' @param devianceFUN the function to be used for computing the deviance; should not be changed for \pkg{lme4} created objects. ##' @param chkREML optional logical indicating \code{object} maybe a REML fit. ##' @param devcomp ##' @return a list with components \item{logLik} and \code{AICtab} where the first is \code{\link{logLik(object)}} and \code{AICtab} is a "table" of AIC, BIC, logLik, deviance, df.residual() values. llikAIC <- function(object, devianceFUN = devCrit, chkREML = TRUE, devcomp = object@devcomp) { llik <- logLik(object) # returns NA for a REML fit - maybe change? AICstats <- { if(chkREML && devcomp$dims[["REML"]]) devcomp$cmp["REML"] # *no* likelihood stats here else { c(AIC = AIC(llik), BIC = BIC(llik), logLik = c(llik), `-2*log(L)` = devianceFUN(object), df.resid = df.residual(object)) } } list(logLik = llik, AICtab = AICstats) } .prt.aictab <- function(aictab, digits = 1) { t.4 <- round(aictab, digits) if (length(aictab) == 1 && names(aictab) == "REML") cat.f("REML criterion at convergence:", t.4) else { ## slight hack to get residual df formatted as an integer t.4F <- format(t.4) t.4F["df.resid"] <- format(t.4["df.resid"]) print(t.4F, quote = FALSE) } } .prt.VC <- function(varcor, digits, comp = "Std.Dev.", corr = any(comp == "Std.Dev."), formatter = format, ...) { # '...' *only* passed to print() cat("Random effects:\n") fVC <- formatVC(varcor, digits=digits, formatter=formatter, comp=comp, corr=corr) print(fVC, quote = FALSE, digits = digits, ...) } .prt.grps <- function(ngrps, nobs) { cat(sprintf("Number of obs: %d, groups: ", nobs), paste(paste(names(ngrps), ngrps, sep = ", "), collapse = "; "), fill = TRUE) } ## FIXME: print header ("Warnings:\n") ? ## change position in output? comes at the very end, could get lost ... .prt.warn <- function(optinfo, summary=FALSE, ...) { if(length(optinfo) == 0) return() # currently, e.g., from refitML() ## check all warning slots: print numbers of warnings (if any) cc <- optinfo$conv$opt msgs <- unlist(optinfo$conv$lme4$messages) ## can't put nmsgs/nwarnings compactly into || expression ## because of short-circuiting nmsgs <- length(msgs) warnings <- optinfo$warnings nwarnings <- length(warnings) if (cc > 0 || nmsgs > 0 || nwarnings > 0) { m <- if (cc==0) { "(OK)" } else if (!is.null(optinfo$message)) { sprintf("(%s)",optinfo$message) } else "" convmsg <- sprintf("optimizer (%s) convergence code: %d %s", optinfo$optimizer, cc, m) if (summary) { cat(convmsg,sprintf("; %d optimizer warnings; %d lme4 warnings", nwarnings,nmsgs),"\n") } else { cat(convmsg, msgs, unlist(warnings), sep="\n") cat("\n") } } } ## options(lme4.summary.cor.max = 20) --> ./hooks.R ## ~~~~~~~~ ## was .summary.cor.max <- 20 a lme4-namespace hidden global variable ## This is modeled a bit after print.summary.lm : ## Prints *both* 'mer' and 'merenv' - as it uses summary(x) mainly ##' @S3method print summary.merMod print.summary.merMod <- function(x, digits = max(3, getOption("digits") - 3), correlation = NULL, symbolic.cor = FALSE, signif.stars = getOption("show.signif.stars"), ranef.comp = c("Variance", "Std.Dev."), ranef.corr = any(ranef.comp == "Std.Dev."), show.resids = TRUE, ...) { .prt.methTit(x$methTitle, x$objClass) .prt.family(x) .prt.call(x$call); cat("\n") .prt.aictab(x$AICtab); cat("\n") if (show.resids) ## need residuals.merMod() rather than residuals(): ## summary.merMod has no residuals method .prt.resids(x$residuals, digits = digits) .prt.VC(x$varcor, digits = digits, useScale = x$useScale, comp = ranef.comp, corr = ranef.corr, ...) .prt.grps(x$ngrps, nobs = x$devcomp$dims[["n"]]) p <- nrow(x$coefficients) if (p > 0) { cat("\nFixed effects:\n") printCoefmat(x$coefficients, # too radical: zap.ind = 3, #, tst.ind = 4 digits = digits, signif.stars = signif.stars) ## do not show correlation when summary(*, correlation=FALSE) was used: hasCor <- !is.null(VC <- x$vcov) && !is.null(VC@factors$correlation) ## FIXME: don't understand the logic here. We can easily ## defend against the problem of missing pre-computed correlation ## function by reconstituting it if necessary ## (e.g. if using merDeriv::vcov.lmerMod), as in commented code below. ## However, we currently have a test (using fit_agridat_archbold, ## see test-methods.R) that fails if we 'fix' this problem ... ## ## if (hasCor && is.null(VC@factors$correlation)) { ## ## defend against merDeriv definition of vcov.lmerMod; reconstruct ## cc <- cov2cor(VC) ## dimnames(cc) <- dimnames(VC) ## Matrix 1.5.2 bug ## VC@factors <- c(VC@factors, list(correlation = cc)) ## } if(is.null(correlation)) { # default cor.max <- getOption("lme4.summary.cor.max") correlation <- hasCor && (isTRUE(x$corrSet) || p <= cor.max) if(!correlation && p > cor.max && is.na(x$corrSet)) { nam <- deparse(substitute(x)) if(length(nam) > 1 || nchar(nam) >= 32) nam <- "...." message(sprintf(paste( "\nCorrelation matrix not shown by default, as p = %d > %d.", "Use print(%s, correlation=TRUE) or", " vcov(%s) if you need it\n", sep = "\n"), p, cor.max, nam, nam)) } } else if(!is.logical(correlation)) stop("'correlation' must be NULL or logical") if(correlation) { if(is.null(VC)) VC <- vcov(x, correlation = TRUE) corF <- VC@factors$correlation if (is.null(corF)) { # can this still happen? message("\nCorrelation of fixed effects could have been required in summary()") corF <- cov2cor(VC) } p <- ncol(corF) if (p > 1) { rn <- rownames(x$coefficients) rns <- abbreviate(rn, minlength = 11) cat("\nCorrelation of Fixed Effects:\n") if (is.logical(symbolic.cor) && symbolic.cor) { corf <- as(corF, "matrix") dimnames(corf) <- list(rns, abbreviate(rn, minlength = 1, strict = TRUE)) print(symnum(corf)) } else { corf <- matrix(format(round(corF@x, 3), nsmall = 3), ncol = p, dimnames = list(rns, abbreviate(rn, minlength = 6))) corf[!lower.tri(corf)] <- "" print(corf[-1, -p, drop = FALSE], quote = FALSE) } ## !symbolic.cor } ## if (p > 1) } ## if (correlation) } ## if (p>0) if(length(x$fitMsgs) && any(nchar(x$fitMsgs) > 0)) { cat("fit warnings:\n"); writeLines(x$fitMsgs) } .prt.warn(x$optinfo,summary=FALSE) invisible(x) }## print.summary.merMod ##' @S3method print merMod print.merMod <- function(x, digits = max(3, getOption("digits") - 3), correlation = NULL, symbolic.cor = FALSE, signif.stars = getOption("show.signif.stars"), ranef.comp = "Std.Dev.", ranef.corr = any(ranef.comp == "Std.Dev."), ...) { dims <- x@devcomp$dims .prt.methTit(methTitle(dims), class(x)) .prt.family(famlink(x, resp = x@resp)) .prt.call(x@call, long = FALSE) ## useScale <- as.logical(dims[["useSc"]]) llAIC <- llikAIC(x) .prt.aictab(llAIC$AICtab, 4) varcor <- VarCorr(x) .prt.VC(varcor, digits = digits, comp = ranef.comp, corr = ranef.corr, ...) ngrps <- vapply(x@flist, nlevels, 0L) .prt.grps(ngrps, nobs = dims[["n"]]) if(length(cf <- fixef(x)) > 0) { cat("Fixed Effects:\n") print.default(format(cf, digits = digits), print.gap = 2L, quote = FALSE, ...) } else cat("No fixed effect coefficients\n") fitMsgs <- .merMod.msgs(x) if(any(nchar(fitMsgs) > 0)) { cat("fit warnings:\n"); writeLines(fitMsgs) } .prt.warn(x@optinfo,summary=TRUE) invisible(x) } ##' @exportMethod show setMethod("show", "merMod", function(object) print.merMod(object)) ##' Return the deviance component list devcomp <- function(x) { .Deprecated("getME(., \"devcomp\")") stopifnot(is(x, "merMod")) x@devcomp } ##' @exportMethod getL setMethod("getL", "merMod", function(x) { .Deprecated("getME(., \"L\")") getME(x, "L") }) ##' used by tnames mkPfun <- function(diag.only = FALSE, old = TRUE, prefix = NULL){ local({ function(g,e) { mm <- outer(e,e,paste,sep = ".") if(old) { diag(mm) <- e } else { mm[] <- paste(mm,g,sep = "|") if (!is.null(prefix)) mm[] <- paste(prefix[2],mm,sep = "_") diag(mm) <- paste(e,g,sep = "|") if (!is.null(prefix)) diag(mm) <- paste(prefix[1],diag(mm),sep = "_") } mm <- if (diag.only) diag(mm) else mm[lower.tri(mm,diag = TRUE)] if(old) paste(g,mm,sep = ".") else mm } }) } ##' Construct names of individual theta/sd:cor components ##' ##' @param object a fitted model ##' @param diag.only include only diagonal elements? ##' @param old (logical) give backward-compatible results? ##' @param prefix a character vector with two elements giving the prefix ##' for diagonal (e.g. "sd") and off-diagonal (e.g. "cor") elements tnames <- function(object, diag.only = FALSE, old = TRUE, prefix = NULL) { pfun <- mkPfun(diag.only=diag.only, old=old, prefix=prefix) c(unlist(mapply(pfun, names(object@cnms), object@cnms))) } ## -> ../man/getME.Rd getME <- function(object, name, ...) UseMethod("getME") ##' Extract or Get Generalized Components from a Fitted Mixed Effects Model getME.merMod <- function(object, name = c("X", "Z","Zt", "Ztlist", "mmList", "y", "mu", "u", "b", "Gp", "Tp", "L", "Lambda", "Lambdat", "Lind", "Tlist", "A", "RX", "RZX", "sigma", "flist", "fixef", "beta", "theta", "ST", "REML", "is_REML", "n_rtrms", "n_rfacs", "N", "n", "p", "q", "p_i", "l_i", "q_i", "k", "m_i", "m", "cnms", "devcomp", "offset", "lower", "devfun", "glmer.nb.theta" ), ...) { if(missing(name)) stop("'name' must not be missing") ## Deal with multiple names -- "FIXME" is inefficiently redoing things if (length(name <- as.character(name)) > 1) { names(name) <- name return(lapply(name, getME, object = object)) } if(name == "ALL") ## recursively get all provided components return(sapply(eval(formals()$name), getME.merMod, object=object, simplify=FALSE)) stopifnot(is(object,"merMod")) name <- match.arg(name) rsp <- object@resp PR <- object@pp dc <- object@devcomp th <- object@theta cnms <- object@cnms dims <- dc $ dims Tpfun <- function(cnms) { ltsize <- function(n) n*(n+1)/2 # lower triangle size cLen <- cumsum(ltsize(lengths(cnms))) setNames(c(0, cLen), c("beg__", names(cnms))) ## such that diff( Tp ) is well-named } ## mmList. <- mmList(object) if(any(name == c("p_i", "q_i", "m_i"))) p_i <- vapply(mmList(object), ncol, 1L) if(any(name == c("l_i", "q_i"))) l_i <- vapply(object@flist, nlevels, 1L) switch(name, "X" = PR$X, ## ok ? - check -- use model.matrix() method instead? "Z" = t(PR$Zt), "Zt" = PR$Zt, "Ztlist" = { getInds <- function(i) { n <- diff(object@Gp)[i] ## number of elements in this block nt <- length(cnms[[i]]) ## number of REs inds <- lapply(seq(nt), seq, to = n, by = nt) ## pull out individual RE indices inds <- lapply(inds,function(x) x + object@Gp[i]) ## add group offset } inds <- do.call(c,lapply(seq_along(cnms),getInds)) setNames(lapply(inds,function(i) PR$Zt[i,]), tnames(object,diag.only = TRUE)) }, "mmList" = mmList.merMod(object), "y" = rsp$y, "mu" = rsp$mu, "u" = object@u, "b" = crossprod(PR$Lambdat, object@u), # == Lambda %*% u "L" = PR$ L(), "Lambda" = t(PR$ Lambdat), "Lambdat" = PR$ Lambdat, "A" = PR$Lambdat %*% PR$Zt, "Lind" = PR$ Lind, "RX" = structure(PR$RX(), dimnames = list(colnames(PR$X), colnames(PR$X))), ## maybe add names elsewhere? "RZX" = structure(PR$RZX, dimnames = list(NULL, colnames(PR$X))), ## maybe add names elsewhere? "sigma" = sigma(object), "Gp" = object@Gp, "Tp" = Tpfun(cnms), # "term-wise theta pointer" "flist" = object@flist, "fixef" = fixef(object), "beta" = object@beta, "theta" = setNames(th, tnames(object)), "ST" = setNames(vec2STlist(object@theta, n = lengths(cnms)), names(cnms)), "Tlist" = { nc <- lengths(cnms) # number of columns per term nt <- length(nc) # number of random-effects terms ans <- vector("list",nt) names(ans) <- names(nc) pos <- 0L for (i in 1:nt) { # will need modification for more general Lambda nci <- nc[i] tt <- matrix(0., nci, nci) inds <- lower.tri(tt, diag=TRUE) nthi <- sum(inds) tt[inds] <- th[pos + seq_len(nthi)] pos <- pos + nthi ans[[i]] <- tt } ans }, "REML" = dims[["REML"]], "is_REML" = isREML(object), ## number of random-effects terms "n_rtrms" = length(cnms), ## number of random-effects grouping factors "n_rfacs" = length(object@flist), "N" = dims[["N"]], "n" = dims[["n"]], "p" = dims[["p"]], "q" = dims[["q"]], "p_i" = p_i, "l_i" = l_i, "q_i" = p_i * l_i, "k" = length(cnms), "m_i" = choose(p_i + 1, 2), "m" = dims[["nth"]], "cnms" = cnms, "devcomp" = dc, "offset" = rsp$offset, "lower" = setNames(object@lower, tnames(object)), "devfun" = { verbose <- getCall(object)$verbose; if (is.null(verbose)) verbose <- 0L if (isGLMM(object)) { reTrms <- getME(object,c("Zt","theta","Lambdat","Lind","flist","cnms")) d1 <- mkGlmerDevfun(object@frame, getME(object,"X"), reTrms=reTrms, family(object), verbose=verbose) nAGQ <- object@devcomp$dims[["nAGQ"]] updateGlmerDevfun(d1, reTrms, nAGQ=nAGQ) } else { ## copied from refit ... DRY ... devlist <- list(pp=PR, resp=rsp, u0=PR$u0, dpars=seq_along(PR$theta), verbose=verbose) mkdevfun(rho=list2env(devlist), ## FIXME: fragile ... // also pass 'maxit' ? verbose=verbose, control=object@optinfo$control) } }, ## FIXME: current version gives lower bounds for theta parameters only: ## -- these must be extended for [GN]LMMs -- give extended value including -Inf values for beta values? "glmer.nb.theta" = if(isGLMM(object) && isNBfamily(rsp$family$family)) getNBdisp(object) else NA, "..foo.." = # placeholder! stop(gettextf("'%s' is not implemented yet", sprintf("getME(*, \"%s\")", name))), ## otherwise stop(sprintf("Mixed-Effects extraction of '%s' is not available for class \"%s\"", name, class(object)))) }## {getME} ##' @importMethodsFrom Matrix t %*% crossprod diag tcrossprod ##' @importClassesFrom Matrix dgCMatrix dpoMatrix corMatrix NULL ## Extract the conditional variance-covariance matrix of the fixed-effects ## parameters vcov.merMod <- function(object, correlation = TRUE, sigm = sigma(object), use.hessian = NULL, full = FALSE, noScale = NULL, ...) { ## FIXME: warn/message if GLMM (RX-computation is approximate), ## if other vars are specified? if (full) return(vcov_full(object, sigm)) hess.avail <- ## (1) numerical Hessian computed? (!is.null(h <- object@optinfo$derivs$Hessian) && ## (2) does Hessian include fixed-effect parameters? nrow(h) > (ntheta <- length(getME(object,"theta")))) if (is.null(use.hessian)) use.hessian <- hess.avail if (use.hessian && !hess.avail) stop(shQuote("use.hessian"), "=TRUE specified, ", "but Hessian is unavailable") calc.vcov.hess <- function(h) { ## invert 2*Hessian, catching errors and forcing symmetric result ## ~= forceSymmetric(solve(h/2)[i,i]) : solve(h/2) = 2*solve(h) h <- tryCatch(solve(h), error=function(e) matrix(NA,nrow=nrow(h),ncol=ncol(h))) i <- -seq_len(ntheta) ## drop var-cov parameters h <- h[i,i] forceSymmetric(h + t(h)) } ## alternately: calculate var-cov from implicit (RX) information ## provided by fit (always the case for lmerMods) V <- sigm^2 * object@pp$unsc() if (hess.avail) { V.hess <- calc.vcov.hess(h) bad.V.hess <- any(is.na(V.hess)) if (!bad.V.hess) { ## another 'bad var-cov' check: positive definite? e.hess <- eigen(V.hess,symmetric = TRUE,only.values = TRUE)$values if (min(e.hess) <= 0) bad.V.hess <- TRUE } } if (!use.hessian && hess.avail) { ## if hessian is available, go ahead and check ## for similarity with the RX-based estimate var.hess.tol <- 1e-4 # FIXME: should var.hess.tol be user controlled? if (!bad.V.hess && any(abs(V-V.hess) > var.hess.tol * V.hess)) warning("variance-covariance matrix computed ", "from finite-difference Hessian\nand ", "from RX differ by >",var.hess.tol,": ", "consider ",shQuote("use.hessian=TRUE")) } if (use.hessian) { if (!bad.V.hess) { V <- V.hess } else { warning("variance-covariance matrix computed ", "from finite-difference Hessian is\n", "not positive definite or contains NA values: falling back to ", "var-cov estimated from RX") } } ## FIXME: try to catch non-PD matrices rr <- tryCatch(as(V, "dpoMatrix"), error = function(e)e) if (inherits(rr, "error")) { warning(gettextf("Computed variance-covariance matrix problem: %s;\nreturning NA matrix", rr$message), domain = NA) rr <- matrix(NA,nrow(V),ncol(V)) } nmsX <- colnames(object@pp$X) dimnames(rr) <- list(nmsX,nmsX) if(correlation) rr@factors$correlation <- if(!is.na(sigm)) as(rr, "corMatrix") else rr # (is NA anyway) ## If auto-scaling is enabled if(is.null(noScale) || (!is.null(noScale) && !noScale)){ if (!is.null(sc <- attr(object@pp$X, "scaled:scale"))) { ce <- attr(object@pp$X, "scaled:center") rr <- scale_vcov(rr, sc, ce) } } rr } vcov_full <- function(object, s = sigma(object)) { L <- getME(object, "L") RX <- getME(object, "RX") RZX <- getME(object, "RZX") Lambdat <- getME(object, "Lambdat") RXtinv <- solve(t(RX)) LinvLambdat <- solve(L, Lambdat, system = "L") Minv <- s * rbind( cbind(LinvLambdat, Matrix(0, nrow = nrow(L), ncol = ncol(RX))), cbind(-RXtinv %*% t(RZX) %*% LinvLambdat, RXtinv) ) Cmat <- crossprod(Minv) ## do we have machinery elsewhere for this (names for b-vector) ? ## should this be extracted into a utility f'n (e.g. for getME(., "b") ? fix_nms <- colnames(object@pp$X) rr <- ranef(object, condVar = FALSE) gnms <- function(x) c(outer(colnames(x), rownames(x), function(x,y) paste(y, x, sep = "."))) rnms <- lapply(rr, gnms) re_nms <- unlist(Map(function(n, r) paste(n, r, sep = "."), names(rr), rnms)) all_nms <- unname(c(re_nms, fix_nms)) dimnames(Cmat) <- list(all_nms, all_nms) return(Cmat) } ##' @importFrom stats vcov ##' @S3method vcov summary.merMod vcov.summary.merMod <- function(object, ...) { if(is.null(object$vcov)) stop("logic error in summary of merMod object") object$vcov } ##' Make variance and correlation matrices from \code{theta} ##' ##' @param sc scale factor (residual standard deviation) ##' @param cnms component names ##' @param nc numeric vector: number of terms in each RE component ##' @param theta theta vector (lower-triangle of Cholesky factors) ##' @param nms component names (FIXME: nms/cnms redundant: nms=names(cnms)?) ##' @seealso \code{\link{VarCorr}} ##' @return A matrix ##' @export mkVarCorr <- function(sc, cnms, nc, theta, nms) { ncseq <- seq_along(nc) thl <- split(theta, rep.int(ncseq, (nc * (nc + 1))/2)) if(!all(nms == names(cnms))) ## the above FIXME warning("nms != names(cnms) -- whereas lme4-authors thought they were --\n", "Please report!", immediate. = TRUE) ans <- lapply(ncseq, function(i) { ## Li := \Lambda_i, the i-th block diagonal of \Lambda(\theta) Li <- diag(nrow = nc[i]) Li[lower.tri(Li, diag = TRUE)] <- thl[[i]] rownames(Li) <- cnms[[i]] ## val := \Sigma_i = \sigma^2 \Lambda_i \Lambda_i', the val <- tcrossprod(sc * Li) # variance-covariance stddev <- sqrt(diag(val)) corr <- t(val / stddev)/stddev diag(corr) <- 1 structure(val, stddev = stddev, correlation = corr) }) if(is.character(nms)) { ## FIXME: do we want this? Maybe not. ## Potential problem: the names of the elements of the VarCorr() list ## are not necessarily unique (e.g. fm2 from example("lmer") has *two* ## Subject terms, so the names are "Subject", "Subject". The print method ## for VarCorrs handles this just fine, but it's a little awkward if we ## want to dig out elements of the VarCorr list ... ??? if (anyDuplicated(nms)) nms <- make.names(nms, unique = TRUE) names(ans) <- nms } structure(ans, sc = sc) } ##' Extract variance and correlation components ##' VarCorr.merMod <- function(x, sigma = 1, ...) { ## TODO: now that we have '...', add type=c("varcov","sdcorr","logs" ? if (is.null(cnms <- x@cnms)) stop("VarCorr methods require reTrms, not just reModule") if(missing(sigma)) sigma <- sigma(x) nc <- lengths(cnms) # no. of columns per term structure(mkVarCorr(sigma, cnms = cnms, nc = nc, theta = x@theta, nms = { fl <- x@flist; names(fl)[attr(fl, "assign")]}), useSc = as.logical(x@devcomp$dims[["useSc"]]), class = "VarCorr.merMod") } if(FALSE)## *NOWHERE* used _FIXME_ ?? ## Compute standard errors of fixed effects from an merMod object ## ## @title Standard errors of fixed effects ## @param object "merMod" object, ## @param ... additional, optional arguments. None are used at present. ## @return numeric vector of length length(fixef(.)) unscaledVar <- function(object, ...) { stopifnot(is(object, "merMod")) sigma(object) * diag(object@pp$unsc()) } ##' @S3method print VarCorr.merMod print.VarCorr.merMod <- function(x, digits = max(3, getOption("digits") - 2), comp = "Std.Dev.", corr = any(comp == "Std.Dev."), formatter = format, ...) { print(formatVC(x, digits=digits, comp=comp, corr=corr, formatter=formatter), quote = FALSE, ...) invisible(x) } ##' "format()" the 'VarCorr' matrix of the random effects -- for ##' print()ing and show()ing ##' ##' @title Format the 'VarCorr' Matrix of Random Effects ##' @param varc a \code{\link{VarCorr}} (-like) matrix with attributes. ##' @param digits the number of significant digits. ##' @param comp character vector of length one or two indicating which ##' columns out of "Variance" and "Std.Dev." should be shown in the ##' formatted output. ##' @param formatter the \code{\link{function}} to be used for ##' formatting the standard deviations and or variances (but ##' \emph{not} the correlations which (currently) are always formatted ##' as "0.nnn" ##' @param ... optional arguments for \code{formatter(*)} in addition ##' to the first (numeric vector) and \code{digits}. ##' @return a character matrix of formatted VarCorr entries from \code{varc}. formatVC <- function(varcor, digits = max(3, getOption("digits") - 2), comp = "Std.Dev.", corr = any(comp == "Std.Dev."), formatter = format, useScale = attr(varcor, "useSc"), ...) { c.nms <- c("Groups", "Name", "Variance", "Std.Dev.") avail.c <- c.nms[-(1:2)] if(anyNA(mcc <- pmatch(comp, avail.c))) stop("Illegal 'comp': ", comp[is.na(mcc)]) nc <- length(colnms <- c(c.nms[1:2], (use.c <- avail.c[mcc]))) if(length(use.c) == 0) stop("Must show variances and/or standard deviations") reStdDev <- c(lapply(varcor, attr, "stddev"), if(useScale) list(Residual = unname(attr(varcor, "sc")))) reLens <- lengths(reStdDev) nr <- sum(reLens) reMat <- array('', c(nr, nc), list(rep.int('', nr), colnms)) reMat[1+cumsum(reLens)-reLens, "Groups"] <- names(reLens) reMat[,"Name"] <- c(unlist(lapply(varcor, colnames)), if(useScale) "") if(any("Variance" == use.c)) reMat[,"Variance"] <- formatter(unlist(reStdDev)^2, digits = digits, ...) if(any("Std.Dev." == use.c)) reMat[,"Std.Dev."] <- formatter(unlist(reStdDev), digits = digits, ...) if (any(reLens > 1L)) { ## append lower triangular matrix of correlations / covariances maxlen <- max(reLens) Lcomat <- if(corr) lapply(varcor, attr, "correlation") else # just the matrix, i.e. {dim,dimnames} lapply(varcor, identity) ## function(v) `attributes<-`(v, attributes(v)[c("dim","dimnames")]) co <- # corr or cov do.call(rbind, lapply(Lcomat, function(x) { x <- as.matrix(x) dig <- max(2, digits - 2) # use 'digits' ! ## not using formatter() for correlations cc <- format(round(x, dig), nsmall = dig) cc[!lower.tri(cc)] <- "" nr <- nrow(cc) if (nr >= maxlen) return(cc) cbind(cc, matrix("", nr, maxlen-nr)) }))[, -maxlen, drop = FALSE] if (nrow(co) < nrow(reMat)) co <- rbind(co, matrix("", nrow(reMat) - nrow(co), ncol(co))) colnames(co) <- c(if(corr) "Corr" else "Cov", rep.int("", max(0L, ncol(co)-1L))) cbind(reMat, co, deparse.level=0L) } else reMat } ##' @S3method summary merMod summary.merMod <- function(object, correlation = (p <= getOption("lme4.summary.cor.max")), use.hessian = NULL, ...) { if (...length() > 0) { ## FIXME: need testing code warning("additional arguments ignored") } ## se.calc: hess.avail <- (!is.null(h <- object@optinfo$derivs$Hessian) && nrow(h) > length(getME(object,"theta"))) if (is.null(use.hessian)) use.hessian <- hess.avail if (use.hessian && !hess.avail) stop("'use.hessian=TRUE' specified, but Hessian is unavailable") resp <- object@resp devC <- object@devcomp dd <- devC$dims ## cmp <- devC$cmp useSc <- as.logical(dd[["useSc"]]) sig <- sigma(object) ## REML <- isREML(object) famL <- famlink(resp = resp) p <- length(coefs <- fixef(object)) ## protect against merDeriv's vcov.glmerMod(), which only ## handles binomial and Poisson values if (is(object, "glmerMod") && !family(object)$family %in% c("binomial", "poisson")) { vcov <- vcov.merMod } vc <- vcov(object, use.hessian = use.hessian) stdError <- sqrt(diag(vc)) coefs <- cbind("Estimate" = coefs, "Std. Error" = stdError) if (p > 0) { coefs <- cbind(coefs, (cf3 <- coefs[,1]/coefs[,2]), deparse.level = 0) colnames(coefs)[3] <- paste(if(useSc) "t" else "z", "value") if (isGLMM(object)) # FIXME: if "t" above, cannot have "z" here coefs <- cbind(coefs, "Pr(>|z|)" = 2*pnorm(abs(cf3), lower.tail = FALSE)) } llAIC <- llikAIC(object) ## FIXME: You can't count on object@re@flist, ## nor compute VarCorr() unless is(re, "reTrms"): varcor <- VarCorr(object) # use S3 class for now structure(list(methTitle = methTitle(dd), objClass = class(object), devcomp = devC, isLmer = is(resp, "lmerResp"), useScale = useSc, logLik = llAIC[["logLik"]], family = famL$family, link = famL$link, ngrps = ngrps(object), coefficients = coefs, sigma = sig, ## explicitly call method to avoid getting m ## essed up by merDeriv's vcov method ## (which doesn't assign a VC attribute) vcov = vcov.merMod(object, correlation = correlation, sigm = sig), varcor = varcor, # and use formatVC(.) for printing. AICtab = llAIC[["AICtab"]], call = object@call, residuals = residuals(object,"pearson",scaled = TRUE), fitMsgs = .merMod.msgs(object), optinfo = object@optinfo, ## put corrSet **last** so we don't mess up people relying on numeric indexing ## of elements (!!) corrSet = if(!missing(correlation)) correlation else NA # TRUE/FALSE (when set) / NA ), class = "summary.merMod") } ## TODO: refactor? Why the hell do we need a summary method for summary.merMod? ##' @S3method summary summary.merMod summary.summary.merMod <- function(object, varcov = TRUE, ...) { if(varcov && is.null(object$vcov)) object$vcov <- vcov.merMod(object, correlation = TRUE, sigm = object$sigma) object } ##' @importFrom stats weights ##' @S3method weights merMod weights.merMod <- function(object, type = c("prior","working"), ...) { type <- match.arg(type) isPrior <- type == "prior" if(!isGLMM(object) && !isPrior) stop("working weights only available for GLMMs") res <- if(isPrior) object@resp$weights else object@pp$Xwts^2 ## the working weights available through pp$Xwts should be ## equivalent to: ## object@resp$weights*(object@resp$muEta()^2)/object@resp$variance() ## however, the unit tests in tests/glmmWeights.R suggest that this ## equivalence is approximate. this may be fine, however, if the ## discrepancy is due to another instance of the general problem of ## reference class fields not being updated at the optimum, then this ## could cause real problems. see for example: ## https://github.com/lme4/lme4/issues/166 ## FIXME: what to do about missing values (see stats:::weights.glm)? ## FIXME: add unit tests return(res) } ## utility function: x is a ranef.mer object, nx is the name of an element asDf0 <- function(x,nx,id=FALSE) { xt <- x[[nx]] ss <- stack(xt) ss$ind <- factor(as.character(ss$ind), levels = colnames(xt)) ss$.nn <- rep.int(reorder(factor(rownames(xt)), xt[[1]], FUN = mean,sort = sort), ncol(xt)) ## allow 'postVar' *or* 'condVar' names pv <- attr(xt,"postVar") if (is.null(pv)) { pv <- attr(xt,"condVar") } if (!is.null(pv)) { tmpfun <- function(pvi) { unlist(lapply(1:nrow(pvi), function(i) sqrt(pvi[i, i, ]))) } if (!is.list(pv)) { ss$se <- tmpfun(pv) } else { ## rely on ordering when unpacking! ss$se <- unlist(lapply(pv,tmpfun)) } } if (id) ss$id <- nx return(ss) } ## convert ranef object to a long-format data frame, e.g. suitable ## for ggplot2 (or homemade lattice plots) ## FIXME: have some gymnastics to do if terms, levels are different ## for different grouping variables - want to maintain ordering ## but still allow rbind()ing as.data.frame.ranef.mer <- function(x, ...) { xL <- lapply(names(x), asDf0, x=x, id=TRUE) ## combine xD <- do.call(rbind,xL) ## rename ... oldnames <- c("values", "ind", ".nn", "se", "id") newnames <- c("condval","term","grp", "condsd","grpvar") names(xD) <- newnames[match(names(xD),oldnames)] ## reorder ... neworder <- c("grpvar","term","grp","condval") if ("condsd" %in% names(xD)) neworder <- c(neworder,"condsd") xD[neworder] } dim.merMod <- function(x) { getME(x, c("n", "p", "q", "p_i", "l_i", "q_i", "k", "m_i", "m")) } ## Internal utility, only used in optwrap() : ##' @title Get the optimizer function and check it minimally ##' @param optimizer character string ( = function name) *or* function getOptfun <- function(optimizer) { if (((is.character(optimizer) && optimizer == "optimx") || deparse(substitute(optimizer)) == "optimx")) { if (!requireNamespace("optimx")) { stop(shQuote("optimx")," package must be installed order to ", "use ",shQuote('optimizer="optimx"')) } optfun <- optimx::optimx } else if (is.character(optimizer)) { optfun <- tryCatch(get(optimizer), error = function(e) NULL) } else optfun <- optimizer if (is.null(optfun)) stop("couldn't find optimizer function ",optimizer) if (!is.function(optfun)) stop("non-function specified as optimizer") needArgs <- c("fn","par","lower","control") if (anyNA(match(needArgs, names(formals(optfun))))) stop("optimizer function must use (at least) formal parameters ", paste(sQuote(needArgs), collapse = ", ")) optfun } optwrap <- function(optimizer, fn, par, lower = -Inf, upper = Inf, control = list(), adj = FALSE, calc.derivs = TRUE, force.calc.derivs = FALSE, use.last.params = FALSE, verbose = 0L) { ## calc.derivs may be passed as NULL by some upstream pkgs ... calc.derivs <- calc.derivs %||% TRUE ## control must be specified if adj==TRUE; ## otherwise this is a fairly simple wrapper optfun <- getOptfun(optimizer) optName <- if(is.character(optimizer)) optimizer else ## "good try": deparse(substitute(optimizer))[[1L]] lower <- rep_len(lower, length(par)) upper <- rep_len(upper, length(par)) if (adj) ## control parameter tweaks: only for second round in nlmer, glmer switch(optName, "bobyqa" = { if(!is.numeric(control$rhobeg)) control$rhobeg <- 0.0002 if(!is.numeric(control$rhoend)) control$rhoend <- 2e-7 }, "Nelder_Mead" = { if (is.null(control$xst)) { thetaStep <- 0.1 nTheta <- length(environment(fn)$pp$theta) betaSD <- sqrt(diag(environment(fn)$pp$unsc())) control$xst <- 0.2* c(rep.int(thetaStep, nTheta), pmin(betaSD, 10)) } if (is.null(control$xt)) control$xt <- control$xst*5e-4 }) switch(optName, "bobyqa" = { if(all(par == 0)) par[] <- 0.001 ## minor kludge if(!is.numeric(control$iprint)) control$iprint <- min(verbose, 3L) }, "Nelder_Mead" = control$verbose <- verbose, "nloptwrap" = control$print_level <- min(as.numeric(verbose),3L), ## otherwise: if(verbose) warning(gettextf( "'verbose' not yet passed to optimizer '%s'; consider fixing optwrap()", optName), domain = NA) ) arglist <- list(fn = fn, par = par, lower = lower, upper = upper, control = control) ## optimx: must pass method in control (?) because 'method' was previously ## used in lme4 to specify REML vs ML if (optName == "optimx") { if (is.null(method <- control$method)) stop("must specify 'method' explicitly for optimx") arglist$control$method <- NULL arglist <- c(arglist, list(method = method)) } ## FIXME: test! effects of multiple warnings?? ## may not need to catch warnings after all?? curWarnings <- list() opt <- withCallingHandlers(do.call(optfun, arglist), warning = function(w) { curWarnings <<- append(curWarnings,list(w$message)) }) ## cat("***",unlist(tail(curWarnings,1))) ## FIXME: set code to warn on convergence !=0 ## post-fit tweaking if (optName == "bobyqa") { opt$convergence <- opt$ierr } else if (optName == "Nelder_Mead") { ## fix-up: Nelder_Mead treats running out of iterations as "convergence" (!?) if (opt$NM.result==4) opt$convergence <- 4 } else if (optName == "optimx") { opt <- list(par = coef(opt)[1,], fvalues = opt$value[1], method = method, conv = opt$convcode[1], feval = opt$fevals + opt$gevals, message = attr(opt,"details")[,"message"][[1]]) } if ((optconv <- getConv(opt)) != 0) { wmsg <- paste("convergence code",optconv,"from",optName) if (!is.null(getMsg(opt))) wmsg <- paste0(wmsg,": ",getMsg(opt)) warning(wmsg) curWarnings <<- append(curWarnings,list(wmsg)) } ## pp_before <- environment(fn)$pp ## save(pp_before,file="pp_before.RData") singular <- any(opt$par[lower==0] < getSingTol()) if (force.calc.derivs || (calc.derivs && !singular)){ if (use.last.params) { ## +0 tricks R into doing a deep copy ... ## otherwise element of ref class changes! ## FIXME:: clunky!! orig_pars <- opt$par orig_theta <- environment(fn)$pp$theta+0 orig_pars[seq_along(orig_theta)] <- orig_theta } if (verbose > 10) cat("computing derivatives\n") derivs <- deriv12(fn, opt$par, fx = opt$value) if (use.last.params) { ## run one more evaluation of the function at the optimized ## value, to reset the internal/environment variables in devfun ... fn(orig_pars) } } else derivs <- NULL if (!use.last.params) { ## run one more evaluation of the function at the optimized ## value, to reset the internal/environment variables in devfun ... fn(opt$par) } structure(opt, ## store all auxiliary information optimizer = optimizer, control = control, warnings = curWarnings, derivs = derivs) } as.data.frame.VarCorr.merMod <- function(x,row.names = NULL, optional = FALSE, order = c("cov.last", "lower.tri"), ...) { order <- match.arg(order) tmpf <- function(v,grp) { vcov <- c(diag(v), v[lt.v <- lower.tri(v, diag = FALSE)]) sdcor <- c(attr(v,"stddev"), attr(v,"correlation")[lt.v]) nm <- rownames(v) n <- nrow(v) dd <- data.frame(grp = grp, var1 = nm[c(seq(n), col(v)[lt.v])], var2 = c(rep(NA,n), nm[row(v)[lt.v]]), vcov, sdcor, stringsAsFactors = FALSE) if (order=="lower.tri") { ## reorder *back* to lower.tri order m <- matrix(NA,n,n) diag(m) <- seq(n) m[lower.tri(m)] <- (n+1):(n*(n+1)/2) dd <- dd[m[lower.tri(m, diag=TRUE)],] } dd } r <- do.call(rbind, c(mapply(tmpf, x,names(x), SIMPLIFY = FALSE), deparse.level = 0)) if (attr(x,"useSc")) { ss <- attr(x,"sc") r <- rbind(r,data.frame(grp = "Residual",var1 = NA,var2 = NA, vcov = ss^2, sdcor = ss), deparse.level = 0) } rownames(r) <- NULL r } lme4/R/modular.R0000644000176200001440000011200615113136605013103 0ustar liggesusers#### --> ../man/modular.Rd #### ================== ### Small utilities to be used in lFormula() and glFormula() doCheck <- function(x) { is.character(x) && !any(x == "ignore") } RHSForm <- function(form,as.form=FALSE) { rhsf <- form[[length(form)]] if (as.form) reformulate(deparse(rhsf)) else rhsf } `RHSForm<-` <- function(formula,value) { formula[[length(formula)]] <- value formula } ##' Original formula, minus response ( = '~ ') : noLHSform <- function(formula) { if (length(formula)==2) formula else formula[-2] } ##' @param cstr name of control being set ##' @param val value of control being set checkCtrlLevels <- function(cstr, val, smallOK=FALSE) { bvals <- c("message","warning","stop","ignore") if (smallOK) bvals <- outer(bvals, c("","Small"), paste0) if (!is.null(val) && !val %in% bvals) stop("invalid control level ",sQuote(val)," in ",cstr,": valid options are {", paste(sapply(bvals,sQuote),collapse=","),"}") invisible(NULL) } ## general identifiability checker, used both in checkZdim and checkZrank wmsg <- function(n, cmp.val, allow.n, msg1="", msg2="", msg3="") { if (allow.n) { ## allow n == cmp.val unident <- n < cmp.val cmp <- "<" rstr <- "" } else { unident <- n <= cmp.val cmp <- "<=" rstr <- " and the residual variance (or scale parameter)" } ## %s without spaces intentional (don't want an extra space if the ## message component is empty) wstr <- sprintf("%s (=%d) %s %s (=%d)%s; the random-effects parameters%s are probably unidentifiable", msg1, n, cmp,msg2, cmp.val,msg3, rstr) list(unident=unident, wstr=wstr) } ##' For each r.e. term, test if Z has more columns than rows to detect ##' unidentifiability: ##' @title ##' @param Ztlist list of Zt matrices - one for each r.e. term ##' @param n no. observations ##' @param ctrl ##' @param allow.n allow as many random-effects as there are observations ##' for each term? ##' @return possibly empty character string with warning messages checkZdims <- function(Ztlist, n, ctrl, allow.n=FALSE) { stopifnot(is.list(Ztlist), is.numeric(n)) cstr <- "check.nobs.vs.nRE" checkCtrlLevels(cstr, cc <- ctrl[[cstr]]) term.names <- names(Ztlist) rows <- vapply(Ztlist, nrow, 1L) cols <- vapply(Ztlist, ncol, 1L) stopifnot(all(cols == n)) if (doCheck(cc)) { unique(unlist(lapply(seq_along(Ztlist), function(i) { ww <- wmsg(cols[i], rows[i], allow.n, "number of observations", "number of random effects", sprintf(" for term (%s)", term.names[i])) if(ww$unident) { switch(cc, "warning" = warning(ww$wstr, call.=FALSE), "stop" = stop (ww$wstr, call.=FALSE), stop(gettextf("unknown check level for '%s'", cstr), domain=NA)) ww$wstr } else character() }))) ## -> possibly empty vector of error messages } else character() } ##' @importFrom Matrix rankMatrix checkZrank <- function(Zt, n, ctrl, nonSmall = 1e6, allow.n=FALSE) { stopifnot(is.list(ctrl), is.numeric(n), is.numeric(nonSmall)) cstr <- "check.nobs.vs.rankZ" if (doCheck(cc <- ctrl[[cstr]])) { ## not NULL or "ignore" checkCtrlLevels(cstr, cc, smallOK=TRUE) d <- dim(Zt) doTr <- d[1L] < d[2L] # Zt is "wide" => qr needs transpose(Zt) if(!(grepl("Small",cc) && prod(d) > nonSmall)) { rankZ <- rankMatrix(if(doTr) t(Zt) else Zt, method="qr") ww <- wmsg(n, rankZ, allow.n, "number of observations", "rank(Z)") if(is.na(rankZ)) { cc <- "stop" ww <- list(unident = TRUE, wstr = sub("^.*;", "rank(Z) is NA: invalid random effect factors?", ww$wstr)) } if (ww$unident) { switch(cc, "warningSmall" =, "warning" = warning(ww$wstr,call.=FALSE), "stopSmall" =, "stop" = stop(ww$wstr,call.=FALSE), stop(gettextf("unknown check level for '%s'", cstr), domain=NA)) ww$wstr } else character() } else character() } else character() } ## check scale of non-dummy columns of X, both ## against each other and against 1 (implicit scale of theta parameters)? ## (shouldn't matter for lmer models?) ## TODO: check for badly centred models? ## TODO: check scale of Z columns? ## What should the rules be? try to find problematic columns ## and rescale them? scale+center? Just scale or scale+center ## all numeric columns? ## checkScaleX <- function(X, kind="warning", tol=1e3) { ## cstr <- "check.scaleX" kinds <- eval(formals(lmerControl)[["check.scaleX"]]) if (!kind %in% kinds) stop(gettextf("unknown check-scale option: %s",kind)) if (is.null(kind) || kind == "ignore") return(X) ## else : cont.cols <- apply(X,2,function(z) !all(z %in% c(0,1))) col.sd <- apply(X[,cont.cols, drop=FALSE], 2L, sd) sdcomp <- outer(col.sd,col.sd,"/") logcomp <- abs(log(sdcomp[lower.tri(sdcomp)])) logsd <- abs(log(col.sd)) if (any(c(logcomp,logsd) > log(tol))) { wmsg <- "Some predictor variables are on very different scales:" if (kind %in% c("warning","stop")) { msg2 <- "\nYou may also use (g)lmerControl(autoscale = TRUE) to improve numerical stability." wmsg <- paste(wmsg, "consider rescaling.", msg2) switch(kind, "warning" = warning(wmsg, call.=FALSE), "stop" = stop(wmsg, call.=FALSE)) } else { wmsg <- paste(wmsg, "auto-rescaled (results NOT adjusted)") ## mimic scale() because we don't want to make a copy in ## order to retrieve the center/scale X[,cont.cols] <- sweep(X[,cont.cols,drop=FALSE],2,col.sd,"/") attr(X,"scaled:scale") <- setNames(col.sd,colnames(X)[cont.cols]) if (kind == "warn+rescale") warning(wmsg, call.=FALSE) } } else wmsg <- character() structure(X, msgScaleX = wmsg) } ##' @title Check that grouping factors have at least 2 and 1 sampled level" switch(cc, "warning" = warning(wstr,call.=FALSE), "stop" = stop(wstr,call.=FALSE), ## FIXME: should never get here since we have checkCtrLevels test above? stop(gettextf("unknown check level for '%s'", cstr), domain=NA)) } else wstr <- character() ## Part 2 ---------------- cstr <- "check.nobs.vs.nlev" checkCtrlLevels(cstr, cc <- ctrl[[cstr]]) if (doCheck(cc) && any(if(allow.n) nlevelVec > n else nlevelVec >= n)) { ## figure out which factors are the problem? w <- if (allow.n) which(nlevelVec>n) else which(nlevelVec>=n) bad_facs <- names(nlevelVec)[w] wst2 <- gettextf( "number of levels of each grouping factor must be %s number of observations", if(allow.n) "<=" else "<") wst2 <- paste0(wst2," (problems: ",paste(bad_facs,collapse=", "),")") switch(cc, "warning" = warning(wst2, call.=FALSE), "stop" = stop(wst2, call.=FALSE) ## shouldn't reach here ) } else wst2 <- character() ## Part 3 ---------------- cstr <- "check.nlev.gtreq.5" checkCtrlLevels(cstr, cc <- ctrl[[cstr]]) if (doCheck(cc) && any(nlevelVec < 5)) { wst3 <- "grouping factors with < 5 sampled levels may give unreliable estimates" switch(cc, "warning" = warning(wst3, call.=FALSE), "stop" = stop (wst3, call.=FALSE), stop(gettextf("unknown check level for '%s'", cstr), domain=NA)) } else wst3 <- character() ## return: c(wstr, wst2, wst3) ## possibly == character(0) } ##' Coefficients (columns) are dropped from a design matrix to ##' ensure that it has full rank. ##' ##' Redundant columns of the design matrix are identified with the ##' LINPACK implementation of the \code{\link{qr}} decomposition and ##' removed. The returned design matrix will have \code{qr(X)$rank} ##' columns. ##' ##' (Note: I have lifted this function from the ordinal (soon Rufus) ##' package and modified it slightly./rhbc) ##' ##' @title Ensure full rank design matrix ##' @param X a design matrix, e.g., the result of ##' \code{\link{model.matrix}} possibly of less than full column rank, ##' i.e., with redundant parameters. ##' @param silent should a message not be issued if X is column rank ##' deficient? ##' @return The design matrix \code{X} without redundant columns. ##' @seealso \code{\link{qr}} and \code{\link{lm}} ##' @importFrom Matrix rankMatrix ##' @author Rune Haubo Bojesen Christensen (drop.coef()); Martin Maechler chkRank.drop.cols <- function(X, kind, tol = 1e-7, method = "qr") { ## Test and match arguments: stopifnot(is.matrix(X)) # i.e., *not* sparse kinds <- eval(formals(lmerControl)[["check.rankX"]]) if (!kind %in% kinds) stop(gettextf("undefined option for 'kind': %s", kind)) ## c("message+drop.cols", "ignore", ## "silent.drop.cols", "warn+drop.cols", "stop.deficient"), if(kind == "ignore") return(X) ## else : p <- ncol(X) if (kind == "stop.deficient") { if ((rX <- rankMatrix(X, tol=tol, method=method)) < p) stop(gettextf(sub("\n +", "\n", "the fixed-effects model matrix is column rank deficient (rank(X) = %d < %d = p); the fixed effects will be jointly unidentifiable"), rX, p), call. = FALSE) } else { ## kind is one of "message+drop.cols", "silent.drop.cols", "warn+drop.cols" ## --> consider to drop extraneous columns: "drop.cols": ## Perform the qr-decomposition of X using LINPACK method, ## as we need the "good" pivots (and the same as lm()): ## this rankMatrix(X, method="qrLINPACK"): FIXME? rankMatrix(X, method= "qr.R") qr.X <- qr(X, tol = tol, LAPACK = FALSE) rnkX <- qr.X$rank if (rnkX == p) return(X) ## return X if X has full column rank ## else: ## message about no. dropped columns: msg <- sprintf(ngettext(p - rnkX, "fixed-effect model matrix is rank deficient so dropping %d column / coefficient", "fixed-effect model matrix is rank deficient so dropping %d columns / coefficients"), p - rnkX) if (kind != "silent.drop.cols") (if(kind == "warn+drop.cols") warning else message)(msg, domain = NA) ## Save properties of X contr <- attr(X, "contrasts") asgn <- attr(X, "assign") ## Return the columns correponding to the first qr.x$rank pivot ## elements of X: keep <- qr.X$pivot[seq_len(rnkX)] dropped.names <- colnames(X[,-keep,drop=FALSE]) X <- X[, keep, drop = FALSE] if (rankMatrix(X, tol=tol, method=method) < ncol(X)) stop(gettextf("Dropping columns failed to produce full column rank design matrix"), call. = FALSE) ## Re-assign relevant attributes: if(!is.null(contr)) attr(X, "contrasts") <- contr if(!is.null(asgn)) attr(X, "assign") <- asgn[keep] attr(X, "msgRankdrop") <- msg attr(X, "col.dropped") <- setNames(qr.X$pivot[(rnkX+1L):p], dropped.names) } X } # check that response is not constant checkResponse <- function(y, ctrl) { stopifnot(is.list(ctrl)) cstr <- "check.response.not.const" checkCtrlLevels(cstr, cc <- ctrl[[cstr]]) if (doCheck(cc) && length(unique(y)) < 2L) { wstr <- "Response is constant" switch(cc, "warning" = warning(wstr, call.=FALSE), "stop" = stop(wstr, call.=FALSE), stop(gettextf("unknown check level for '%s'", cstr), domain=NA)) } else character() } ##' Extract all warning msgs from a merMod object .merMod.msgs <- function(x) { ## currently only those found with 'X' : aX <- attributes(x@pp$X) wmsgs <- grep("^msg", names(aX)) if(any(has.msg <- nchar(Xwmsgs <- unlist(aX[wmsgs])) > 0)) Xwmsgs[has.msg] else character() } ## NA predict and restore rownames of original data if necessary ## napredictx <- function(x,...) { ## res <- napredict(x) ## } ##' @rdname modular ##' @param control a list giving (for \code{[g]lFormula}) all options (see \code{\link{lmerControl}} for running the model; ##' (for \code{mkLmerDevfun,mkGlmerDevfun}) options for inner optimization step; ##' (for \code{optimizeLmer} and \code{optimize[Glmer}) control parameters for nonlinear optimizer (typically inherited from the \dots argument to \code{lmerControl}) ##' @return \bold{lFormula, glFormula}: A list containing components, ##' \item{fr}{model frame} ##' \item{X}{fixed-effect design matrix} ##' \item{reTrms}{list containing information on random effects structure: result of \code{\link{mkReTrms}}} ##' \item{REML}{(lFormula only): logical flag: use restricted maximum likelihood? (Copy of argument.)} ##' @export lFormula <- function(formula, data=NULL, REML = TRUE, subset, weights, na.action, offset, contrasts = NULL, control=lmerControl(), ...) { control <- control$checkControl ## this is all we really need mf <- mc <- match.call() dontChk <- c("start", "verbose", "devFunOnly") dots <- list(...) do.call(checkArgs, c(list("lmer"), dots[!names(dots) %in% dontChk])) if (!is.null(dots[["family"]])) { ## lmer(...,family=...); warning issued within checkArgs mc[[1]] <- quote(lme4::glFormula) if (missing(control)) mc[["control"]] <- glmerControl() return(eval(mc, parent.frame())) } cstr <- "check.formula.LHS" checkCtrlLevels(cstr,control[[cstr]]) denv <- checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop") #mc$formula <- formula <- as.formula(formula,env=denv) ## substitute evaluated call formula <- as.formula(formula, env=denv) ## as.formula ONLY sets environment if not already explicitly set. ## ?? environment(formula) <- denv # get rid of || terms so update() works as expected RHSForm(formula) <- reformulas::expandDoubleVerts(RHSForm(formula)) mc$formula <- formula ## (DRY! copied from glFormula) m <- match(c("data", "subset", "weights", "na.action", "offset"), names(mf), 0L) mf <- mf[c(1L, m)] mf$drop.unused.levels <- TRUE mf[[1L]] <- quote(stats::model.frame) fr.form <- reformulas::subbars(formula) # substitute "|" by "+" environment(fr.form) <- environment(formula) ## model.frame.default looks for these objects in the environment ## of the *formula* (see 'extras', which is anything passed in '...'), ## so they have to be put there: for (i in c("weights", "offset")) { if (!eval(bquote(missing(x=.(i))))) assign(i,get(i,parent.frame()),environment(fr.form)) } mf$formula <- fr.form fr <- eval(mf, parent.frame()) if (nrow(fr) == 0L) stop("0 (non-NA) cases") ## convert character vectors to factor (defensive) fr <- factorize(fr.form, fr, char.only=TRUE) ## store full, original formula & offset attr(fr,"formula") <- formula attr(fr,"offset") <- mf$offset n <- nrow(fr) ## random effects and terms modules reTrms <- reformulas::mkReTrms(reformulas::findbars(RHSForm(formula)), fr) wmsgNlev <- checkNlevels(reTrms$flist, n=n, control) wmsgZdims <- checkZdims(reTrms$Ztlist, n=n, control, allow.n=FALSE) if (anyNA(reTrms$Zt)) { stop("NA in Z (random-effects model matrix): ", "please use ", shQuote("na.action='na.omit'"), " or ", shQuote("na.action='na.exclude'")) } wmsgZrank <- checkZrank(reTrms$Zt, n=n, control, nonSmall = 1e6) ## fixed-effects model matrix X - remove random effect parts from formula: fixedform <- formula RHSForm(fixedform) <- reformulas::nobars(RHSForm(fixedform)) mf$formula <- fixedform ## re-evaluate model frame to extract predvars component fixedfr <- eval(mf, parent.frame()) attr(attr(fr,"terms"), "predvars.fixed") <- attr(attr(fixedfr,"terms"), "predvars") ## so we don't have to fart around retrieving which vars we need ## in model.frame(.,fixed.only=TRUE) attr(attr(fr,"terms"), "varnames.fixed") <- names(fixedfr) ## ran-effects model frame (for predvars) ## important to COPY formula (and its environment)? ranform <- formula RHSForm(ranform) <- reformulas::subbars(RHSForm(reOnly(formula))) mf$formula <- ranform ranfr <- eval(mf, parent.frame()) attr(attr(fr,"terms"), "predvars.random") <- attr(terms(ranfr), "predvars") ## FIXME: shouldn't we have this already in the full-frame predvars? X <- model.matrix(fixedform, fr, contrasts)#, sparse = FALSE, row.names = FALSE) ## sparseX not yet ## Scaling (if autoscale is on...) if (!is.null(control$autoscale) && control$autoscale) { if("(Intercept)" %in% colnames(X)){ X_scaled <- scale(X[, -1]) X[,-1] <- X_scaled } else { X_scaled <- scale(X) X <- X_scaled } attr(X, "scaled:center") <- attr(X_scaled, "scaled:center") attr(X, "scaled:scale") <- attr(X_scaled, "scaled:scale") } ## backward compatibility (keep no longer than ~2015): if(is.null(rankX.chk <- control[["check.rankX"]])) rankX.chk <- eval(formals(lmerControl)[["check.rankX"]])[[1]] X <- chkRank.drop.cols(X, kind=rankX.chk, tol = 1e-7) if(is.null(scaleX.chk <- control[["check.scaleX"]])) scaleX.chk <- eval(formals(lmerControl)[["check.scaleX"]])[[1]] X <- checkScaleX(X, kind=scaleX.chk) list(fr = fr, X = X, reTrms = reTrms, REML = REML, formula = formula, wmsgs = c(Nlev = wmsgNlev, Zdims = wmsgZdims, Zrank = wmsgZrank)) } ## utility f'n for checking starting values getStart <- function(start, pred, returnVal = c("theta","all")) { returnVal <- match.arg(returnVal) doFixef <- returnVal == "all" ## default values theta <- pred$theta fixef <- pred$delb if (is.numeric(start)) { theta <- start } else if (is.list(start)) { if (!all(vapply(start, is.numeric, NA))) stop("all elements of start must be numeric") if (length((badComp <- setdiff(names(start), c("theta","fixef")))) > 0) stop("incorrect components in start list: ", badComp) if (!is.null(start$theta)) theta <- start$theta if (doFixef) { noBeta <- is.null(start$beta) if(!is.null(sFE <- start$fixef) || !noBeta) { fixef <- if(!is.null(sFE)) { if(!noBeta) message("Starting values for fixed effects coefficients", "specified through both 'fixef' and 'beta',", "only 'fixef' used") ## FIXME? accumulating heuristic evidence for drop1()-like case if((p <- length(fixef)) < length(sFE) && p == ncol(pred$X) && is.character(ns <- names(sFE)) && all((cnX <- dimnames(pred$X)[[2L]]) %in% ns)) ## take "matching" fixef[] only sFE[cnX] else sFE } else if(!noBeta) start$beta } if (length(fixef)!=length(pred$delb)) stop("incorrect number of fixef components (!=",length(pred$delb),")") } } else if (!is.null(start)) stop("'start' must be NULL, a numeric vector or named list of such vectors") if (!is.null(start) && length(theta) != length(pred$theta)) stop("incorrect number of theta components (!=",length(pred$theta),")") if(doFixef) c(theta, fixef) else theta } ## update start ## should refactor this to ## turn numeric start into start=list(theta=start) immediately ?? updateStart <- function(start, theta) { if (is.numeric(start)) { theta } else { if (!is.null(start$theta)) start$theta <- theta start } } ##' @rdname modular ##' @param fr A model frame containing the variables needed to create an ##' \code{\link{lmerResp}} or \code{\link{glmResp}} instance ##' @param X fixed-effects design matrix ##' @param reTrms information on random effects structure (see \code{\link{mkReTrms}}) ##' @param REML (logical) fit restricted maximum likelihood model? ##' @param start starting values ##' @param verbose print output? ##' @return \bold{mkLmerDevfun, mkGlmerDevfun}: A function to calculate deviance ##' (or restricted deviance) as a function of the theta (random-effect) parameters ##' (for GlmerDevfun, of beta (fixed-effect) parameters as well). These deviance ##' functions have an environment containing objects required for their evaluation. ##' CAUTION: The output object of \code{mk(Gl|L)merDevfun} is an \code{\link{environment}} ##' containing reference class objects (see \code{\link{ReferenceClasses}}, \code{\link{merPredD-class}}, ##' \code{\link{lmResp-class}}), which behave in ways that may surprise many users. For example, if the ##' output of \code{mk(Gl|L)merDevfun} is naively copied, then modifications to the original will ##' also appear in the copy (and vice versa). To avoid this behavior one must make a deep copy ##' (see \code{\link{ReferenceClasses}} for details). ##' \cr ##' \cr ##' @export mkLmerDevfun <- function(fr, X, reTrms, REML = TRUE, start = NULL, verbose = 0, control = lmerControl(), ...) { ## FIXME: make sure verbose gets handled properly #if (missing(fr)) { ## reconstitute frame #} ## pull necessary arguments for making the model frame out of ... p <- ncol(X) # maybe also do rank check on X here?? rho <- new.env(parent=parent.env(environment())) rho$pp <- do.call(merPredD$new, c(reTrms[c("Zt","theta","Lambdat","Lind")], n=nrow(X), list(X=X))) REMLpass <- if(REML) p else 0L rho$resp <- if(missing(fr)) mkRespMod( REML = REMLpass, ...) else mkRespMod(fr, REML = REMLpass) ## FIXME / note: REML does double duty as rank of X and a flag for using ## REML maybe this should be mentioned in the help file for ## mkRespMod?? currently that help file says REML is logical. a ## consequence of this double duty is that it is impossible to fit ## a model with no fixed effects using REML (MM: ==> FIXME) ## devfun <- mkdevfun(rho, 0L, verbose=verbose, control=control) ## prevent R CMD check false pos. warnings (in this function only): pp <- resp <- NULL rho$lmer_Deviance <- lmer_Deviance devfun <- function(theta) .Call(lmer_Deviance, pp$ptr(), resp$ptr(), as.double(theta)) environment(devfun) <- rho # if all random effects are of the form 1|f and starting values not # otherwise provided (and response variable is present, i.e. not doing # a simulation) then compute starting values if (is.null(start) && all(reTrms$cnms == "(Intercept)") && length(reTrms$flist) == length(reTrms$lower) && !is.null(y <- model.response(fr))) { v <- sapply(reTrms$flist, function(f) var(ave(y, f))) v.e <- var(y) - sum(v) if (!is.na(v.e) && v.e > 0) { v.rel <- v / v.e if (all(v.rel >= reTrms$lower^2)) rho$pp$setTheta(sqrt(v.rel)) } } ## theta <- getStart(start, rho$pp) ## ^^^^^ unused / obfuscation? should the above be rho$pp$setTheta(.) ? ## MM: commenting it did not break any of our checks if (length(rho$resp$y) > 0) ## only if non-trivial y devfun(rho$pp$theta) # one evaluation to ensure all values are set rho$lower <- reTrms$lower # to be more consistent with mkGlmerDevfun devfun # this should pass the rho environment implicitly } ##' @param devfun a deviance function, as generated by \code{\link{mkLmerDevfun}} ##' @return \bold{optimizeLmer}: Results of an optimization. optimizeLmer <- function(devfun, optimizer= formals(lmerControl)$optimizer, restart_edge= formals(lmerControl)$restart_edge, boundary.tol = formals(lmerControl)$boundary.tol, start = NULL, verbose = 0L, control = list(), ...) { verbose <- as.integer(verbose) rho <- environment(devfun) opt <- optwrap(optimizer, devfun, getStart(start, rho$pp), lower=rho$lower, control=control, adj=FALSE, verbose=verbose, ...) if (restart_edge) { ## FIXME: should we be looking at rho$pp$theta or opt$par ## at this point??? in koller example (for getData(13)) we have ## rho$pp$theta=0, opt$par=0.08 if (length(bvals <- which(rho$pp$theta==rho$lower)) > 0) { ## *don't* use numDeriv -- cruder but fewer dependencies, no worries ## about keeping to the interior of the allowed space theta0 <- new("numeric",rho$pp$theta) ## 'deep' copy: d0 <- devfun(theta0) btol <- 1e-5 ## FIXME: make user-settable? bgrad <- sapply(bvals, function(i) { bndval <- rho$lower[i] theta <- theta0 theta[i] <- bndval+btol (devfun(theta)-d0)/btol }) ## what do I need to do to reset rho$pp$theta to original value??? devfun(theta0) ## reset rho$pp$theta after tests ## FIXME: allow user to specify ALWAYS restart if on boundary? if (any(is.na(bgrad))) { warning("some gradient components are NA near boundaries, skipping boundary check") return(opt) } else { if (any(bgrad < 0)) { if (verbose) message("some theta parameters on the boundary, restarting") opt <- optwrap(optimizer, devfun, opt$par, lower=rho$lower, control=control, adj=FALSE, verbose=verbose, ...) } } ## bgrad not NA } } ## if restart.edge if (boundary.tol > 0) check.boundary(rho, opt, devfun, boundary.tol) else opt } ## TODO: remove any arguments that aren't actually used by glFormula (same for lFormula) ## TODO(?): lFormula() and glFormula() are very similar: merge or use common baseFun() ##' @rdname modular ##' @inheritParams glmer ##' @export glFormula <- function(formula, data=NULL, family = gaussian, subset, weights, na.action, offset, contrasts = NULL, start, mustart, etastart, control = glmerControl(), ...) { ## FIXME: does start= do anything? test & fix control <- control$checkControl ## this is all we really need mf <- mc <- match.call() ## extract family, call lmer for gaussian if (is.character(family)) family <- get(family, mode = "function", envir = parent.frame(2)) if( is.function(family)) family <- family() if (isTRUE(all.equal(family, gaussian()))) { mc[[1]] <- quote(lme4::lFormula) mc["family"] <- NULL # to avoid an infinite loop return(eval(mc, parent.frame())) } if (family$family %in% c("quasibinomial", "quasipoisson", "quasi")) stop('"quasi" families cannot be used in glmer') dontChk <- c("verbose", "devFunOnly", "optimizer", "nAGQ") dots <- list(...) do.call(checkArgs, c(list("glmer"), dots[!names(dots) %in% dontChk])) cstr <- "check.formula.LHS" checkCtrlLevels(cstr, control[[cstr]]) denv <- checkFormulaData(formula, data, checkLHS = control$check.formula.LHS == "stop") mc$formula <- formula <- as.formula(formula, env = denv) ## substitute evaluated version ## DRY ... m <- match(c("data", "subset", "weights", "na.action", "offset", "mustart", "etastart"), names(mf), 0L) mf <- mf[c(1L, m)] mf$drop.unused.levels <- TRUE mf[[1L]] <- quote(stats::model.frame) fr.form <- reformulas::subbars(formula) # substitute "|" by "+" environment(fr.form) <- environment(formula) ## model.frame.default looks for these objects in the environment ## of the *formula* (see 'extras', which is anything passed in '...'), ## so they have to be put there: for (i in c("weights", "offset")) { if (!eval(bquote(missing(x=.(i))))) assign(i, get(i, parent.frame()), environment(fr.form)) } mf$formula <- fr.form fr <- eval(mf, parent.frame()) ## convert character vectors to factor (defensive) fr <- factorize(fr.form, fr, char.only = TRUE) ## store full, original formula & offset attr(fr,"formula") <- formula attr(fr,"offset") <- mf$offset ## attach starting coefficients to model frame so we can ## pass them through to mkRespMod -> family()$initialize ... if (!missing(start) && is.list(start)) { fixef <- start$fixef %||% start$beta attr(fr,"start") <- fixef } n <- nrow(fr) ## random effects and terms modules reTrms <- reformulas::mkReTrms(reformulas::findbars(RHSForm(formula)), fr) ## TODO: allow.n = !useSc {see FIXME below} wmsgNlev <- checkNlevels(reTrms$ flist, n = n, control, allow.n = TRUE) wmsgZdims <- checkZdims(reTrms$Ztlist, n = n, control, allow.n = TRUE) wmsgZrank <- checkZrank(reTrms$ Zt, n = n, control, nonSmall = 1e6, allow.n = TRUE) ## FIXME: adjust test for families with estimated scale parameter: ## useSc is not defined yet/not defined properly? ## if (useSc && maxlevels == n) ## stop("number of levels of each grouping factor must be", ## "greater than number of obs") ## fixed-effects model matrix X - remove random effect parts from formula: fixedform <- formula RHSForm(fixedform) <- reformulas::nobars(RHSForm(fixedform)) mf$formula <- fixedform ## re-evaluate model frame to extract predvars component fixedfr <- eval(mf, parent.frame()) attr(attr(fr,"terms"),"predvars.fixed") <- attr(attr(fixedfr,"terms"),"predvars") ## ran-effects model frame (for predvars) ## important to COPY formula (and its environment)? ranform <- formula RHSForm(ranform) <- reformulas::subbars(RHSForm(reOnly(formula))) mf$formula <- ranform ranfr <- eval(mf, parent.frame()) attr(attr(fr,"terms"), "predvars.random") <- attr(terms(ranfr), "predvars") X <- model.matrix(fixedform, fr, contrasts)#, sparse = FALSE, row.names = FALSE) ## sparseX not yet ## Scaling (if autoscale is on...) if (!is.null(control$autoscale) && control$autoscale) { if("(Intercept)" %in% colnames(X)){ X_scaled <- scale(X[, -1]) X[,-1] <- X_scaled } else { X_scaled <- scale(X) X <- X_scaled } attr(X, "scaled:center") <- attr(X_scaled, "scaled:center") attr(X, "scaled:scale") <- attr(X_scaled, "scaled:scale") } ## backward compatibility (keep no longer than ~2015): if(is.null(rankX.chk <- control[["check.rankX"]])) rankX.chk <- eval(formals(lmerControl)[["check.rankX"]])[[1]] X <- chkRank.drop.cols(X, kind=rankX.chk, tol = 1e-7) if(is.null(scaleX.chk <- control[["check.scaleX"]])) scaleX.chk <- eval(formals(lmerControl)[["check.scaleX"]])[[1]] X <- checkScaleX(X, kind=scaleX.chk) list(fr = fr, X = X, reTrms = reTrms, family = family, formula = formula, wmsgs = c(Nlev = wmsgNlev, Zdims = wmsgZdims, Zrank = wmsgZrank)) } ##' @rdname modular ##' @export mkGlmerDevfun <- function(fr, X, reTrms, family, nAGQ = 1L, verbose = 0L, maxit = 100L, control = glmerControl(), ...) { stopifnot(length(nAGQ <- as.integer(nAGQ)) == 1L, 0L <= nAGQ, nAGQ <= 25L) verbose <- as.integer(verbose) maxit <- as.integer(maxit) rho <- list2env(list(verbose=verbose, maxit=maxit, tolPwrss= control$tolPwrss, compDev = control$compDev), parent = parent.frame()) rho$pp <- do.call(merPredD$new, c(reTrms[c("Zt","theta","Lambdat","Lind")], n=nrow(X), list(X=X))) rho$resp <- if (missing(fr)) mkRespMod(family=family, ...) else mkRespMod(fr, family=family) nAGQinit <- if(control$nAGQ0initStep) 0L else 1L ## allow trivial y if (length(y <- rho$resp$y) > 0) { checkResponse(y, control$checkControl) rho$verbose <- as.integer(verbose) ## initialize (from mustart) .Call(glmerLaplace, rho$pp$ptr(), rho$resp$ptr(), nAGQinit, control$tolPwrss, maxit, verbose) rho$lp0 <- rho$pp$linPred(1) # each pwrss opt begins at this eta rho$pwrssUpdate <- glmerPwrssUpdate } rho$lower <- reTrms$lower # not needed in rho? mkdevfun(rho, nAGQinit, maxit=maxit, verbose=verbose, control=control) ## this should pass the rho environment implicitly } ##' @rdname modular ##' @param nAGQ number of Gauss-Hermite quadrature points ##' @param stage optimization stage (1: nAGQ=0, optimize over theta only; 2: nAGQ possibly >0, optimize over theta and beta) ##' @export optimizeGlmer <- function(devfun, optimizer = if(stage == 1) "bobyqa" else "Nelder_Mead", restart_edge=FALSE, boundary.tol = formals(glmerControl)$boundary.tol, verbose = 0L, control = list(), nAGQ = 1L, stage = 1, start = NULL, ...) { ## FIXME: do we need nAGQ here?? or can we clean up? verbose <- as.integer(verbose) rho <- environment(devfun) if (stage == 1) { start <- getStart(start, rho$pp, "theta") adj <- FALSE } else { ## stage == 2 start <- getStart(start, rho$pp, "all") adj <- TRUE } opt <- optwrap(optimizer, devfun, start, rho$lower, control=control, adj=adj, verbose=verbose, ...) if (stage == 1) { rho$control <- attr(opt,"control") rho$nAGQ <- nAGQ } else { ## stage == 2 rho$resp$setOffset(rho$baseOffset) } if (restart_edge) ## FIXME: implement this ... stop("restart_edge not implemented for optimizeGlmer yet") if (boundary.tol > 0) { opt <- check.boundary(rho, opt, devfun, boundary.tol) if(stage != 1) rho$resp$setOffset(rho$baseOffset) } opt } check.boundary <- function(rho,opt,devfun,boundary.tol) { bdiff <- rho$pp$theta - rho$lower[seq_along(rho$pp$theta)] if (any(edgevals <- 0 < bdiff & bdiff < boundary.tol)) { ## try sucessive "close-to-edge parameters" to see ## if we can improve by setting them equal to the boundary pp <- opt$par for (i in which(edgevals)) { tmppar <- pp tmppar[i] <- rho$lower[i] if (devfun(tmppar) < opt$fval) pp[i] <- tmppar[i] } opt$par <- pp opt$fval <- devfun(opt$par) ## re-run to reset (whether successful or not) ## FIXME: derivatives, Hessian etc. (and any other ## opt messages) *not* recomputed } return(opt) } ## only do this function if nAGQ > 0L ##' @rdname modular ##' @export updateGlmerDevfun <- function(devfun, reTrms, nAGQ = 1L){ if (nAGQ > 1L) { if (length(reTrms$flist) != 1L || length(reTrms$cnms[[1]]) != 1L) stop("nAGQ > 1 is only available for models with a single, scalar random-effects term") } rho <- environment(devfun) rho$nAGQ <- nAGQ rho$lower <- c(rho$lower, rep.int(-Inf, length(rho$pp$beta0))) rho$lp0 <- rho$pp$linPred(1) rho$dpars <- seq_along(rho$pp$theta) rho$baseOffset <- forceCopy(rho$resp$offset) # forcing a copy (!) rho$GQmat <- GHrule(nAGQ) rho$fac <- reTrms$flist[[1]] mkdevfun(rho, nAGQ) # does this attach rho to devfun?? } lme4/R/vcconv.R0000644000176200001440000001716115103163201012733 0ustar liggesusers## These files are not currently exported; we are still trying to figure ## out the most appropriate user interface/naming convention/etc ## These functions will become more important, and need to be ## modified/augmented, if we start allowing for varying V-C structures ## (e.g. diagonal matrices, compound symmetry ...) ## In principle we might want to extract or input information: ## 1. as variance-covariance matrices ## 2. as Cholesky factors ## 3. as 'sdcorr' matrices (std dev on diagonal, correlations off diagonal) ## and we might want the structure to be: ## 1. a concatenated vector representing the lower triangles ## (with an attribute carrying the information about group sizes) ## 2. a list of lower-triangle vectors ## 3. a list of matrices ## 4. a block-diagonal matrix ## If we are trying to convert to and from theta vectors, we also ## have to consider whether we are returning scaled Cholesky factors/ ## var-cov matrices or unscaled ones. For the code below I have ## chosen to allow the residual variance etc. to be appended as ## the last element of a variance-covariance vector. (This last ## part is a little less generic than the rest of it.) ##' List of matrices to concatenated vector ##' ##' Convert list of matrices to concatenated vector of lower triangles ##' with an attribute that gives the dimension of each matrix in the ##' original list. This attribute may be used to reconstruct the ##' matrices. ##' ##' @param L list of symmetric, upper-triangular, or lower-triangular ##' square matrices ##' @return A concatenation of the elements in one triangle of each ##' matrix. An attribute \code{"clen"} gives the dimension of each ##' matrix. mlist2vec <- function(L) { if(is.atomic(L)) L <- list(L) n <- vapply(L, nrow, 1L) ## allow for EITHER upper- or lower-triangular input; ## in either case, read off in "lower-triangular" order ## (column-wise) ff <- function(x) { if (all(na.omit(x[iu <- upper.tri(x)] == 0))) t(x[!iu]) else t(x)[!iu] } structure(unlist(lapply(L,ff)), clen = n) } ## Compute dimensions of a square matrix from the size ## of the lower triangle (length as a vector) get_clen <- function(v,n=NULL) { if (is.null(n)) { if (is.null(n <- attr(v,"clen"))) { ## single component n <- (sqrt(8*length(v)+1)-1)/2 } } n } ##' Concatenated vector to list of matrices ##' ##' Convert concatenated vector to list of matrices (lower triangle or ##' symmetric). These matrices could represent Cholesky factors, ##' covariance matrices, or correlation matrices (with standard ##' deviations on the diagonal). ##' ##' @param v concatenated vector ##' @param n FIXME: this has something to do with the dimension of ##' associated matrices. ##' @param symm Return symmetric matrix if \code{TRUE} or ##' lower-triangular if \code{FALSE} ##' @return List of matrices vec2mlist <- function(v,n=NULL,symm=TRUE) { n <- get_clen(v,n) s <- split(v,rep.int(seq_along(n),n*(n+1)/2)) m <- mapply(function(x,n0) { m0 <- diag(nrow=n0) m0[lower.tri(m0,diag=TRUE)] <- x if (symm) m0[upper.tri(m0)] <- t(m0)[upper.tri(m0)] m0 },s,n,SIMPLIFY=FALSE) m } ## Convert concatenated vector to list of ST matrices vec2STlist <- function(v, n = NULL){ ch <- vec2mlist(v, n, FALSE) # cholesky sdiag <- function(x) { ## 'safe' diag() if (length(x)==1) matrix(x,1,1) else diag(x) } lapply(ch, function(L) { ST <- L%*%sdiag(1/sdiag(L)) diag(ST) <- diag(L) ST }) } ##' Standard deviation-correlation matrix to covariance matrix ##' ##' convert 'sdcor' format -- diagonal = std dev, off-diag=cor to and ##' from variance-covariance matrix ##' ##' @param m Standard deviation-correlation matrix ##' @return Covariance matrix sdcor2cov <- function(m) { sd <- diag(m) diag(m) <- 1 m * outer(sd,sd) } ##' Covariance matrix to standard deviation-correlation matrix ##' ##' convert cov to sdcor ##' ##' @param V Covariance matrix ##' @return Standard deviation-correlation matrix cov2sdcor <- function(V) { ## "own version" of cov2cor(): 1. no warning for NA; 2. diagonal = sd(.) p <- (d <- dim(V))[1L] if (!is.numeric(V) || length(d) != 2L || p != d[2L]) stop("'V' is not a square numeric matrix") sd <- sqrt(diag(V)) Is <- 1/sd r <- V r[] <- Is * V * rep(Is, each = p) diag(r) <- sd if (any(is.na(r))) { warning("NA values in sdcor matrix converted to 0") r[is.na(r)] <- 0 } r } ## dmult <- function(m,s) { ## diag(m) <- diag(m)*s ## m ## } ## attempt to compute Cholesky, allow for positive semi-definite cases ## (hackish) safe_chol <- function(m) { if (any(is.na(m)) || all(m==0)) return(m) if (nrow(m)==1) return(sqrt(m)) if (.isDiagonal.sq.matrix(m)) return(diag(sqrt(diag(m)))) ## attempt regular Chol. decomp if (!is.null(cc <- tryCatch(chol(m), error=function(e) NULL))) return(cc) ## ... pivot if necessary ... cc <- suppressWarnings(chol(m,pivot=TRUE)) oo <- order(attr(cc,"pivot")) cc[,oo] ## FIXME: pivot is here to deal with semidefinite cases, ## but results might be returned in a strange format: TEST } ##' Variance-covariance to relative covariance factor ##' ##' from var-cov to scaled Cholesky: ##' ##' @param v Vector of elements from the lower triangle of a ##' variance-covariance matrix. ##' @param n FIXME: see "@param n" above ##' @param s Scale parameter ##' @return Vector of elements from the lower triangle of a relative ##' covariance factor. Vv_to_Cv <- function(v,n=NULL,s=1) { if (!missing(s)) { v <- v[-length(v)] } r <- mlist2vec(lapply(vec2mlist(v,n,symm=TRUE), function(m) t(safe_chol(m/s^2)))) attr(r,"clen") <- get_clen(v,n) r } ##' Standard-deviation-correlation to relative covariance factor ##' ##' from sd-cor to scaled Cholesky ##' ##' @param v Vector of elements from the lower triangle of a ##' standard-deviation-correlation matrix. ##' @param n FIXME: see "@param n" above ##' @param s Scale parameter ##' @return Vector of elements from the lower triangle of a relative ##' covariance factor. Sv_to_Cv <- function(v,n=NULL,s=1) { if (!missing(s)) { v <- v[-length(v)] } r <- mlist2vec(lapply(vec2mlist(v,n,symm=TRUE), function(m) t(safe_chol(sdcor2cov(m)/s^2)))) attr(r,"clen") <- get_clen(v,n) r } ##' Relative covariance factor to variance-covariance ##' ##' from unscaled Cholesky vector to (possibly scaled) ##' variance-covariance vector ##' ##' @param v Vector of elements from the lower triangle of a ##' relative covariance factor. ##' @param n FIXME: see "@param n" above ##' @param s Scale parameter ##' @return Vector of elements from the lower triangle of a ##' variance-covariance matrix. Cv_to_Vv <- function(v,n=NULL,s=1) { r <- mlist2vec(lapply(vec2mlist(v,n,symm=FALSE), function(m) tcrossprod(m)*s^2)) if (!missing(s)) r <- c(r,s^2) attr(r,"clen") <- get_clen(v,n) r } ##' Relative covariance factor to standard-deviation-correlation ##' ##' from unscaled Chol to sd-cor vector ##' ##' @param v Vector of elements from the lower triangle of a ##' relative covariance factor. ##' @param n FIXME: see "@param n" above ##' @param s Scale parameter ##' @return Vector of elements from the lower triangle of a ##' standard-deviation-correlation matrix. Cv_to_Sv <- function(v,n=NULL,s=1) { r <- mlist2vec(lapply(vec2mlist(v,n,symm=FALSE), function(m) cov2sdcor(tcrossprod(m)*s^2))) if (!missing(s)) r <- c(r,s) attr(r,"clen") <- get_clen(v,n) r } ## tests --> ../inst/tests/test-utils.R lme4/R/AllGeneric.R0000644000176200001440000000412115022107260013435 0ustar liggesusers## utilities, these *exported*: ##' @export getL setGeneric("getL", function(x) standardGeneric("getL")) fixed.effects <- function(object, ...) { ## fixed.effects was an alternative name for fixef .Deprecated("fixef") mCall = match.call() mCall[[1]] = as.name("fixef") eval(mCall, parent.frame()) } random.effects <- function(object, ...) { ## random.effects was an alternative name for ranef .Deprecated("ranef") mCall = match.call() mCall[[1]] = as.name("ranef") eval(mCall, parent.frame()) } ## Create a Markov chain Monte Carlo sample from the posterior ## distribution of the parameters ## ## ## @title Create an MCMC sample ## @param object a fitted model object ## @param n number of samples to generate. Defaults to 1; for real use values of 200-1000 are more typical ## @param verbose should verbose output be given? ## @param ... additional, optional arguments (not used) ## @return a Markov chain Monte Carlo sample as a matrix mcmcsamp <- function(object, n = 1L, verbose = FALSE, ...) UseMethod("mcmcsamp") if(getRversion() < "3.3") { sigma <- function(object, ...) UseMethod("sigma") } isREML <- function(x, ...) UseMethod("isREML") isLMM <- function(x, ...) UseMethod("isLMM") isGLMM <- function(x, ...) UseMethod("isGLMM") isNLMM <- function(x, ...) UseMethod("isNLMM") ##' Refit a model using the maximum likelihood criterion ##' ##' This function is primarily used to get a maximum likelihood fit of ##' a linear mixed-effects model for an \code{\link{anova}} comparison. ##' ##' @title Refit a model by maximum likelihood criterion ##' @param x a fitted model, usually of class \code{"\linkS4class{lmerMod}"}, ##' to be refit according to the maximum likelihood criterion ##' @param ... optional additional parameters. None are used at present. ##' @return an object like \code{x} but fit by maximum likelihood ##' @export refitML <- function(x, ...) UseMethod("refitML") refit <- function(object, newresp, ...) UseMethod("refit") if (FALSE) { setGeneric("HPDinterval", function(object, prob = 0.95, ...) standardGeneric("HPDinterval")) } lme4/R/predict.R0000644000176200001440000012645115113136605013103 0ustar liggesusers##' test for no-random-effect specification: TRUE if NA or ~0, other ##' possibilities are NULL or a non-trivial formula isRE <- function(re.form) { isForm <- inherits(re.form, "formula") (is.null(re.form) || isForm || !is.na(re.form)) && (!isForm || length(re.form) != 2L || !identical(re.form[[2L]], 0)) } if(FALSE) { isRE(NA) ## FALSE isRE(~0) ## " isRE(~y+x) ## TRUE isRE(NULL) ## " isRE(~0+x) ## " } ##' Random Effects formula only reOnly <- function(f, response=FALSE) { reformulate(paste0("(", vapply(reformulas::findbars(f), deparse1, ""), ")"), response = if(response && length(f)==3L) f[[2]], env = environment(f)) } ## '...' may contain fixed.only=TRUE, random.only=TRUE, .. get.orig.levs <- function(object, FUN=levels, newdata=NULL, sparse = FALSE, ...) { Terms <- terms(object, data = newdata, ...) mf <- model.frame(object, ...) isFac <- vapply(mf, is.factor, FUN.VALUE=TRUE) ## ignore response variable isFac[attr(Terms,"response")] <- FALSE mf <- mf[isFac] hasSparse <- any(grepl("sparse", names(formals(FUN)))) # check if FUN has sparse argument orig_levs <- if (any(isFac) && hasSparse) lapply(mf, FUN, sparse = sparse) else if(any(isFac) && !hasSparse) lapply(mf, FUN) # else NULL ## if necessary (allow.new.levels ...) add in new levels if (!is.null(newdata)) { for (n in names(mf)) { orig_levs[[n]] <- c(orig_levs[[n]], setdiff(unique(as.character(newdata[[n]])),orig_levs[[n]])) } } ## more clues about factor-ness of terms if(!is.null(orig_levs)) attr(orig_levs,"isFac") <- isFac orig_levs } ##' Force new parameters into a merMod object ##' (should this be an S3 method?): params(object) <- newvalue ##' @param object ##' @param params a list of the form specified by \code{start} in ##' \code{\link{lmer}} or \code{\link{glmer}} (i.e. a list containing ##' theta and/or beta; maybe eventually further parameters ... ##' What updating do we have to do in order to make the resulting object ##' consistent/safe? ##' TODO: What kind of checking of input do we have to do? (Abstract from ##' lmer/glmer code ...) ##' TODO: make sure this gets updated when the parameter structure changes ##' from (theta, beta) to alpha=(theta, beta, phi) ##' @param inplace logical specifying if object should be modified in place; not yet ##' @param subset logical; needs to be true, if only parts of params are to be reset ##' @examples ##' fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ##' fm1M <- setParams(fm1,list(theta=rep(1,3))) ##' getME(fm1M,"theta") ##' getME(fm1,"theta") ## check that original didn't get messed up ##' ## check that @resp and @pp are the only reference class slots ... ##' sapply(slotNames(fm1),function(x) class(slot(fm1,x))) setParams <- function(object, params, inplace=FALSE, subset=FALSE) { pNames <- c("beta","theta") if (object@devcomp$dims["useSc"]) pNames <- c(pNames, "sigma") if (!is.list(params) || length(setdiff(names(params),pNames)) > 0) stop("params should be specifed as a list with elements from ", "{",paste(shQuote(pNames),collapse=", "),"}") if (!subset && length(setdiff(pNames,names(params))) > 0) { warning("some parameters not specified in setParams()") } nbeta <- length(object@pp$beta(1)) ntheta <- length(object@pp$theta) if (!is.null(beta <- params$beta) && length(beta)!=nbeta) stop("length mismatch in beta (",length(beta), "!=",nbeta,")") if (!is.null(theta <- params$theta) && length(theta)!=ntheta) stop("length mismatch in theta (",length(theta), "!=",ntheta,")") matchNames <- function(x,tn,vecname="theta") { if (!is.null(pn <- names(x))) { if (!setequal(pn,tn)) { ## pn.not.tn <- setdiff(pn,tn) ## tn.not.pn <- setdiff(tn,pn) ## TO DO: more detail? stop("mismatch between ",shQuote(vecname)," parameter vector names and internal names (", paste(tn,collapse=","),")") } x <- x[tn] ## reorder } ## no longer issue warning if unnamed ... return(x) } theta <- matchNames(theta,tnames(object),"theta") beta <- matchNames(beta,colnames(getME(object,"X")),"beta") sigma <- params$sigma if(inplace) { stop("modification in place (copy=FALSE) not yet implemented") } else { ## copy : newObj <- object ## make a copy of the reference class slots to ## decouple them from the original object newObj@pp <- newObj@pp$copy() newObj@resp <- newObj@resp$copy() if (!is.null(beta)) { newObj@pp$setBeta0(beta) newObj@beta <- beta } if (!is.null(theta)) { ## where does theta live and how do I set it? ## (1) in .@theta ## (2) in .@pp$theta newObj@theta <- theta newObj@pp$setTheta(theta) } if (!is.null(sigma)) { snm <- if (object@devcomp$dims[["REML"]]) "sigmaREML" else "sigmaML" newObj@devcomp[["cmp"]][snm] <- sigma } return(newObj) } } ##' Make new random effect terms from specified object and new data, ##' possibly omitting some random effect terms ##' @param object fitted model object ##' @param newdata (optional) data frame containing new data ##' @param re.form formula specifying random effect terms to include (NULL=all, ~0) ##' @param na.action ##' ##' @note Hidden; _only_ used (twice) in this file mkNewReTrms <- function(object, newdata, re.form=NULL, na.action=na.pass, allow.new.levels=FALSE, sparse = max(lengths(orig.random.levs)) > 100) { ## construct (fixed) model frame in order to find out whether there are ## missing data/what to do about them ## need rfd to inherit appropriate na.action; need grouping ## variables as well as any covariates that are included ## in RE terms ## FIXME: mfnew is new data frame, rfd is processed new data ## why do we need both/what is each doing/how do they differ? ## rfd is *only* used in mkReTrms ## mfnew is *only* used for its na.action attribute (!) [fixed only] ## using model.frame would mess up matrix-valued predictors (GH #201) fixed.na.action <- NULL re.form <- re.form %||% reOnly(formula(object)) if (is.null(newdata)) { rfd <- mfnew <- model.frame(object) fixed.na.action <- attr(mfnew,"na.action") } else { if (!identical(na.action,na.pass)) { ## only need to re-evaluate for NAs if na.action != na.pass mfnew <- model.frame(delete.response(terms(object, fixed.only=TRUE)), newdata, na.action=na.action) fixed.na.action <- attr(mfnew,"na.action") } ## make sure we pass na.action with new data ## it would be nice to do something more principled like ## rfd <- model.frame(~.,newdata,na.action=na.action) ## but this adds complexities (stored terms, formula, etc.) ## that mess things up later on ... ## rfd <- na.action(get_all_vars(delete.response(terms(object,fixed.only=FALSE)), newdata)) newdata.NA <- newdata if (!is.null(fixed.na.action)) { newdata.NA <- newdata.NA[-fixed.na.action,] } tt <- delete.response(terms(object,random.only=TRUE)) orig.random.levs <- get.orig.levs(object, random.only=TRUE, newdata=newdata.NA) orig.random.cntr <- get.orig.levs(object, random.only=TRUE, FUN=contrasts, sparse=sparse) ## need to let NAs in RE components go through -- they're handled downstream if (inherits(re.form,"formula")) { ## We use the RE terms *from the original model fit* to construct ## the model frame. This is good for preserving predvars information, ## getting interactions constructed correctly, etc etc etc, ## but can fail if a partial RE specification is used and some of the variables ## in the original RE form are missing from 'newdata' ... ## Fill them in as necessary. Filling in NA is OK - these vars won't actually ## be used later ... pv <- attr(tt,"predvars") for (i in 2:(length(pv))) { missvars <- setdiff(all.vars(pv[[i]]), all.vars(re.form)) for (mv in missvars) { newdata.NA[[mv]] <- NA } } } ## see comments about why suppressWarnings() is needed below ... rfd <- suppressWarnings( model.frame(tt, newdata.NA, na.action=na.pass, xlev=orig.random.levs)) ## restore contrasts (why???) ## find *factor* variables involved in terms (left-hand side of RE formula): reset their contrasts ## only interested in components in re.form, not al REs ff <- re.form ## was: formula(object,random.only=TRUE) termvars <- unique(unlist(lapply(reformulas::findbars(ff), function(x) all.vars(x[[2]])))) for (fn in Reduce(intersect, list( names(orig.random.cntr), termvars, names(rfd)))) { ## a non-factor grouping variable *may* sneak in here via simulate(...) if (!is.factor(rfd[[fn]])) rfd[[fn]] <- factor(rfd[[fn]]) contrasts(rfd[[fn]]) <- orig.random.cntr[[fn]] } if (!is.null(fixed.na.action)) attr(rfd,"na.action") <- fixed.na.action ## ## ## need terms to preserve info about spline/orthog polynomial bases ## attr(rfd,"terms") <- terms(object) ## ## ... but variables list messes things up; can we fix it? ## vlist <- lapply(all.vars(terms(object)), as.name) ## attr(attr(rfd,"terms"),"variables") <- as.call(c(quote(list), vlist)) ## ## take out variables that appear *only* in fixed effects ## all.v <- all.vars(delete.response(terms(object,fixed.only=FALSE))) ## ran.v <- vapply(findbars(formula(object)),all.vars,"") ## fix.v <- all.vars(delete.response(terms(object,fixed.only=TRUE))) ## rfd <- model.frame(delete.response(terms(object,fixed.only=FALSE)), ## newdata,na.action=na.action) } if (inherits(re.form, "formula")) { ## DROP values with NAs in fixed effects if (length(fixed.na.action) > 0) { newdata <- newdata[-fixed.na.action,] } ## note: mkReTrms automatically *drops* unused levels ReTrms <- reformulas::mkReTrms(reformulas::findbars(re.form[[2]]), rfd) ## update Lambdat (ugh, better way to do this?) ReTrms <- within(ReTrms,Lambdat@x <- unname(getME(object,"theta")[Lind])) if (!allow.new.levels && any(vapply(ReTrms$flist, anyNA, NA))) stop("NAs are not allowed in prediction data", " for grouping variables unless allow.new.levels is TRUE") ns.re <- names(re <- ranef(object, condVar = FALSE)) nRnms <- names(Rcnms <- ReTrms$cnms) if (!all(nRnms %in% ns.re)) stop("grouping factors specified in re.form that were not present in original model") new_levels <- lapply(ReTrms$flist, function(x) levels(factor(x))) ## fill in/delete levels as appropriate re_x <- Map(function(r,n) levelfun(r,n, allow.new.levels=allow.new.levels), re[names(new_levels)], new_levels) ## pick out random effects values that correspond to ## random effects incorporated in re.form ... ## NB: Need integer indexing, as nRnms can be duplicated: (age|Subj) + (sex|Subj) : hacked_names <- FALSE get_re <- function(rname, cnms) { nms <- names(re[[rname]]) if (identical(cnms,"(Intercept)") && length(nms)==1 && grepl("^s(.*)$",nms)) { ## HACK to allow gamm4 prediction hacked_names <<- TRUE cnms <- nms } miss_names <- setdiff(cnms, nms) if (length(miss_names)>0) { stop("random effects specified in re.form that were not present in original model ", paste(miss_names, collapse=", ")) } t(re_x[[rname]][,cnms]) ## transpose to make sure unlisting works } re_new <- unlist(Map(get_re, nRnms, Rcnms)) ## only issue warning once per prediction ... if (hacked_names) warning("modified RE names for gamm4 prediction") } Zt <- ReTrms$Zt attr(Zt, "na.action") <- attr(re_new, "na.action") <- fixed.na.action list(Zt=Zt, b=re_new, Lambdat = ReTrms$Lambdat, flist = ReTrms$flist) } ##' @param x a random effect (i.e., data frame with rows equal to levels, columns equal to terms ##' @param n vector of new levels levelfun <- function(x, nl.n, allow.new.levels=FALSE) { ## 1. find and deal with new levels new.levels <- setdiff(nl.n, rownames(x)) if (length(new.levels)>0) { if (!allow.new.levels) { max.err.len <- 60 err.str <- paste(new.levels, collapse = ", ") if (nchar(err.str) > max.err.len) { err.str <- substr(err.str, 1, max.err.len) err.str <- gsub(",[^,]*$", ", ...", err.str) } stop("new levels detected in newdata: ", err.str) } ## create an all-zero data frame corresponding to the new set of levels ... nl.n.comb <- union(nl.n, rownames(x)) newx <- as.data.frame(matrix(0, nrow=length(nl.n.comb), ncol=ncol(x), dimnames=list(nl.n.comb, names(x)))) ## then paste in the matching RE values from the original fit/set of levels newx[rownames(x),] <- x x <- newx } ## 2. find and deal with missing old levels ## ... these should have been dropped when making the Z matrices ## etc. in mkReTrms, so we'd better drop them here to match ... if (!all(r.inn <- rownames(x) %in% nl.n)) { x <- x[r.inn,,drop=FALSE] } return(x) } ##' ##' \code{\link{predict}} method for \code{\linkS4class{merMod}} objects ##' ##' @title Predictions from a model at new data values ##' @param object a fitted model object ##' @param newdata data frame for which to evaluate predictions ##' @param newparams new parameters to use in evaluating predictions ##' @param re.form formula for random effects to condition on. If \code{NULL}, ##' include all random effects; if \code{NA} or \code{~0}, ##' include no random effects ##' @param terms a \code{\link{terms}} object - not used at present ##' @param type character string - either \code{"link"}, the default, ##' or \code{"response"} indicating the type of prediction object returned ##' @param allow.new.levels (logical) if FALSE (default), then any new levels ##' (or NA values) detected in \code{newdata} will trigger an error; if TRUE, then ##' the prediction will use the unconditional (population-level) ##' values for data with previously unobserved levels (or \code{NA}s) ##' @param na.action function determining what should be done with missing values for fixed effects in \code{newdata}. The default is to predict \code{NA}: see \code{\link{na.pass}}. ##' @param se.fit A logical value indicating whether the standard errors should be included or not. Default is FALSE. ##' @param ... optional additional parameters. None are used at present. ##' @return a numeric vector of predicted values, unless \code{se.fit=TRUE} (in which case a list with elements \code{fit} (predicted values) and \code{se.fit} is returned) ##' @note There is no option for computing standard errors of predictions because it is difficult to define an efficient method that incorporates uncertainty in the variance parameters; we recommend \code{\link{bootMer}} for this task. ##' @examples ##' (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 |herd), cbpp, binomial)) ##' str(p0 <- predict(gm1)) # fitted values ##' str(p1 <- predict(gm1,re.form=NA)) # fitted values, unconditional (level-0) ##' newdata <- with(cbpp, expand.grid(period=unique(period), herd=unique(herd))) ##' str(p2 <- predict(gm1,newdata)) # new data, all RE ##' str(p3 <- predict(gm1,newdata,re.form=NA)) # new data, level-0 ##' str(p4 <- predict(gm1,newdata,re.form=~(1|herd))) # explicitly specify RE ##' @method predict merMod ##' @export predict.merMod <- function(object, newdata=NULL, newparams=NULL, re.form=NULL, random.only=FALSE, terms=NULL, type=c("link","response"), allow.new.levels=FALSE, na.action=na.pass, se.fit = FALSE, ...) { ## FIXME: appropriate names for result vector? ## FIXME: make sure behaviour is entirely well-defined for NA in grouping factors ## Dealing with NAs: ## we might need to distinguish among ## (i) NAs in original data and in new data ## (ii) na.action possibilities (exclude, fail, omit, pass) ## (iii) na.action setting in original fit and in predict() ## (iii) NAs in (fixed effect) predictors vs RE grouping variables ## (iv) setting of allow.new.level ## NAs in original data (in the fixed or random effects) ## may lead to a model frame within the ## fitted object that is missing rows; if na.exclude was used, ## these will need to be reconstituted in the prediction. ## ## For the most part, 'na.action's used at the predict stage ## (i.e. for newdata) will work on NAs *in the fixed effects* ## without further intervention; 'na.pass' will automatically ## produce NA values in the output, so 'na.exclude' is not really ## necessary (but might get specified anyway) ## ## In the random effects, NAs in newdata will give a population-level ## prediction if allow.new.levels is TRUE; if it's FALSE they give ## an error (although it could be argued that in that case they ## should follow 'na.action' instead ...) if (any(...names() %in% c("ReForm", "REForm", "REform"))) stop("synonyms 'ReForm', 'REForm', 'REform' are deprecated: please use 're.form' instead") if (...length() > 0) warning("unused arguments ignored") type <- match.arg(type) if (!is.null(terms)) stop("terms functionality for predict not yet implemented") if (!is.null(newparams)) object <- setParams(object,newparams) if (is.null(newdata) && is.null(re.form) && is.null(newparams) && !random.only) { ## raw predict() call, just return fitted values ## (inverse-link if appropriate) if (isLMM(object) || isNLMM(object)) { ## make sure we do *NOT* have NAs in fitted object pred <- na.omit(fitted(object)) } else { ## inverse-link pred <- switch(type,response=object@resp$mu, ## == fitted(object), link=object@resp$eta) eta <- object@resp$eta if (is.null(nm <- rownames(model.frame(object)))) nm <- seq_along(pred) names(pred) <- nm } fit.na.action <- NULL ## flow jumps to end for na.predict } else { ## newdata and/or re.form and/or newparams and/or random.only specified fit.na.action <- attr(object@frame,"na.action") ## original NA action nobs <- if (is.null(newdata)) nrow(object@frame) else nrow(newdata) pred <- rep(0,nobs) if (!random.only) { X <- getME(object, "X") X.col.dropped <- attr(X, "col.dropped") ## modified from predict.glm ... if (is.null(newdata)) { ## Use original model 'X' matrix and offset ## orig. offset: will be zero if there are no matches ... offset <- model.offset(model.frame(object)) if (is.null(offset)) offset <- 0 } else { ## new data specified ## evaluate new fixed effect RHS <- formula(substitute(~R, list(R=RHSForm(formula(object,fixed.only=TRUE))))) ## https://github.com/lme4/lme4/issues/414 ## contrasts are not relevant in random effects; ## model.frame.default warns about dropping contrasts ## if (1) xlev is specified and (2) any factors in ## original data frame had contrasts set ## alternative solution: drop contrasts manually ## (could assign to a new variable newdata2 for safety, ## but I don't think newdata ## is used downstream in this function?) ## isFacND <- which(vapply(newdata, is.factor, FUN.VALUE = TRUE)) ## for (j in isFacND) { ## attr(newdata[[j]], "contrasts") <- NULL ## } orig.fixed.levs <- get.orig.levs(object, fixed.only=TRUE, newdata = newdata) mfnew <- suppressWarnings( model.frame(delete.response(terms(object, fixed.only=TRUE, data = newdata)), newdata, na.action = na.action, xlev = orig.fixed.levs)) X <- model.matrix(RHS, data=mfnew, contrasts.arg=attr(X,"contrasts")) ## hack to remove unused interaction levels? ## X <- X[,colnames(X0)] offset <- 0 # rep(0, nrow(X)) tt <- terms(object, data = newdata) if (!is.null(off.num <- attr(tt, "offset"))) { for (i in off.num) offset <- offset + eval(attr(tt,"variables")[[i + 1]], newdata) } ## FIXME?: simplify(no need for 'mfnew'): can this be different from 'na.action'? fit.na.action <- attr(mfnew,"na.action") ## only need to drop if new data specified ... if(is.numeric(X.col.dropped) && length(X.col.dropped) > 0) X <- X[, -X.col.dropped, drop=FALSE] } pred <- drop(X %*% fixef(object)) ## FIXME:: need to unname() ? ## FIXME: is this redundant?? ## if (!is.null(frOffset <- attr(object@frame,"offset"))) ## offset <- offset + eval(frOffset, newdata) pred <- pred+offset } ## end !(random.only) if (isRE(re.form)) { if (is.null(re.form)) re.form <- reOnly(formula(object)) # RE formula only rfd <- if (is.null(newdata)) { ## try to retrieve original data ... fall back to model frame if necessary ## FIXME: this doesn't solve the problem if columns of model frame and data ## diverge (e.g. transformed objects [log(x)], offsets [offset(x)] ... will ## fail farther along tryCatch(getData(object), error = function(e) object@frame) } else newdata newRE <- mkNewReTrms(object, rfd, re.form, na.action=na.action, allow.new.levels=allow.new.levels) REvals <- base::drop(as(newRE$b %*% newRE$Zt, "matrix")) ## only needed if called as simulation? NAs sometimes excluded within mkNewReTrms ... if (length(pred) != length(REvals)) { if (!class(fit.na.action) %in% c("omit", "exclude") && length(fit.na.action)>0) { stop("fixed/RE pred length mismatch") } REvals <- REvals[-fit.na.action] } pred <- pred + REvals if (random.only) { fit.na.action <- attr(newRE$Zt,"na.action") } } if (isGLMM(object) && type=="response") { eta <- pred pred <- object@resp$family$linkinv(pred) } } ## newdata/newparams/re.form ## fill in NAs as appropriate: ## if NAs were detected in original model fit, OR in updated model frame construction ## but DON'T double-NA if raw prediction in the first place if (is.null(newdata)) { fit.na.action <- attr(model.frame(object),"na.action") if (!missing(na.action)) { ## hack to override action where explicitly specified if (!is.null(fit.na.action)) class(fit.na.action) <- class(attr(na.action(NA),"na.action")) } } pred <- napredict(fit.na.action, pred) if (!se.fit) return(pred) if (!isLMM(object)) warning("se.fit computation uses an approximation to estimate the sampling distribution of the parameters") Cmat <- vcov_full(object) n_u <- length(getME(object, "u")) n_beta <- length(getME(object, "beta")) ## FIXME: these need to be fixed (????) if(is.null(newdata)) { X <- getME(object, "X") if(is.null(re.form)) { Z <- getME(object, "Z") } else { if(isRE(re.form)) { ## FIXME: newRE is not computed here Z <- t(newRE$Zt) } else { Z <- Matrix(0, nrow = nrow(X), ncol = n_u) } } } else { if(isRE(re.form)) { Z <- t(newRE$Zt) } else { ## this is inefficient and we could just calculate ## X %*% Cmat[X part only] t(X) instead Z <- Matrix(0, nrow = nrow(X), ncol = n_u) } } if(random.only) X <- Matrix(0, nrow = nrow(Z), ncol = n_beta) ZX <- cbind(Z, X) ## Subsetting Cmat if (ncol(ZX) != nrow(Cmat)) { Cmat_names <- rownames(Cmat) ## Subsetting appears to occur in the case we use newRE; Z_factors <- newRE$flist C_factors <- object@flist cnms <- object@cnms fix_nms <- colnames(object@pp$X) ## Cmat is padded with zeros for new levels if(allow.new.levels){ Cmat_mod <- pad_Cmat(Cmat, C_factors, Z_factors, Cmat_names, cnms) Cmat <- Cmat_mod$Cmat Cmat_names <- Cmat_mod$Cmat_names C_factors <- Cmat_mod$C_factors } is_group_term <- !Cmat_names %in% fix_nms ## looking to compute the groups (factor levels) that are actually ## included in the Z matrix keep_idx <- !is_group_term mask <- unlist(lapply( intersect(names(C_factors), names(Z_factors)), function(grp) { level_mask <- levels(C_factors[[grp]]) %in% levels(Z_factors[[grp]]) rep(level_mask, each = length(cnms[[grp]])) } )) keep_idx[seq_along(mask)] <- is_group_term[seq_along(mask)] & mask # For now, don't worry about keeping it as dpoMatrix? Cmat <- Cmat[keep_idx, keep_idx] #Cmat <- as(Cmat[keep_idx, keep_idx], "dpoMatrix") } res <- list(fit = pred, se.fit = sqrt(quad.tdiag(Cmat, ZX)) ) if (isGLMM(object) && type=="response") { ## pred0 (linear predictor) will have been stored previously in this case ... res$se.fit <- res$se.fit*abs(family(object)$mu.eta(eta)) } res } # end {predict.merMod} ## all possible LHS evaluated values ... simulate.formula_lhs_matrix <- simulate.formula_lhs_numeric <- simulate.formula_lhs_integer <- simulate.formula_lhs_factor <- simulate.formula_lhs_logical <- simulate.formula_lhs_ <- function(object, nsim = 1, seed = NULL, newdata, ...) { ## N.B. *must* name all arguments so that 'object' is missing in .simulateFun() .simulateFun(formula=object, nsim=nsim, seed=seed, newdata=newdata, ...) } simulate.merMod <- function(object, nsim = 1, seed = NULL, use.u = FALSE, re.form=NA, newdata=NULL, newparams=NULL, family=NULL, cluster.rand=rnorm, allow.new.levels=FALSE, na.action=na.pass, ...) { ## FIXME: is there a reason this can't be a copy of .simulateFun ... ? mc <- match.call() mc[[1]] <- quote(lme4::.simulateFun) eval(mc, parent.frame(1L)) } .simulateFun <- function(object, nsim = 1, seed = NULL, use.u = FALSE, re.form=NA, newdata=NULL, newparams=NULL, formula=NULL,family=NULL, cluster.rand=rnorm, weights=NULL, offset=NULL, allow.new.levels=FALSE, na.action=na.pass, cond.sim=TRUE, ...) { if (...length() > 0) warning("unused arguments ignored") if (missing(object) && (is.null(formula) || is.null(newdata) || is.null(newparams))) { stop("if ",sQuote("object")," is missing, must specify all of ", sQuote("formula"),", ",sQuote("newdata"),", and ", sQuote("newparams")) } nullWts <- FALSE if (is.null(weights)) { if (is.null(newdata)) { weights <- weights(object) } else { nullWts <- TRUE # this flags that 'weights' wasn't supplied by the user weights <- rep(1,nrow(newdata)) } } if (missing(object)) { ## construct fake-fitted object from data, params ## copied from glm(): DRY; this all stems from the ## original sin of handling family=gaussian as a special ## case if (is.character(family)) family <- get(family, mode = "function", envir = parent.frame()) if (is.function(family)) family <- family() if (is.null(family) || (family$family=="gaussian" && family$link=="identity")) { lmod <- lFormula(formula,newdata, weights=weights, offset=offset, control=lmerControl(check.formula.LHS="ignore")) devfun <- do.call(mkLmerDevfun, lmod) object <- mkMerMod(environment(devfun), ## (real parameters will be filled in later) opt = list(par=NA,fval=NA,conv=NA), lmod$reTrms, fr = lmod$fr) } else { glmod <- glFormula(formula,newdata,family=family, weights=weights, offset=offset, control=glmerControl(check.formula.LHS="ignore")) devfun <- do.call(mkGlmerDevfun, glmod) object <- mkMerMod(environment(devfun), ## (real parameters will be filled in later) opt = list(par=NA,fval=NA,conv=NA), glmod$reTrms, fr = glmod$fr) } ## would like to do this: ## so predict() -> fitted() -> set default names will work ## instead we have a special case in fitted() ## object@resp$mu <- rep(NA_real_,nrow(model.frame(object))) } stopifnot((nsim <- as.integer(nsim[1])) > 0, is(object, "merMod")) if (!is.null(newparams)) { object <- setParams(object,newparams) } if (!missing(use.u)) { if (!missing(re.form)) { stop("should specify only one of ",sQuote("use.u"), " and ",sQuote("re.form")) } re.form <- if (use.u) NULL else ~0 } if (is.null(re.form)) { # formula w/o response re.form <- reOnly(formula(object)) } if(!is.null(seed)) set.seed(seed) if(!exists(".Random.seed", envir = .GlobalEnv)) runif(1) # initialize the RNG if necessary RNGstate <- .Random.seed sigma <- sigma(object) ## OBSOLETE: no longer use X? ## n <- nrow(X <- getME(object, "X")) ## link <- if (isGLMM(object)) "response" ## predictions, conditioned as specified, on link scale ## previously: do **NOT** use na.action as specified here (inherit ## from object instead, for consistency) ## now: use na.omit, because we have to match up ## with whatever is done in mkNewReTrms etapred <- predict(object, newdata=newdata, re.form=re.form, type="link", na.action=na.omit, allow.new.levels=allow.new.levels) n <- length(etapred) ## now add random components: ## only the ones we did *not* condition on ## compre.form <- noLHSform(formula(object)) ## construct RE formula ONLY: leave out fixed terms, ## which might have loose terms like offsets in them ... ##' combine unary or binary operator + arguments (sugar for 'substitute') makeOp <- function(x,y,op=NULL) { if (is.null(op)) { ## unary substitute(OP(X),list(X=x,OP=y)) } else substitute(OP(X,Y), list(X=x,OP=op,Y=y)) } compReForm <- reOnly(formula(object)) if (isRE(re.form)) { rr <- reOnly(re.form)[[2]] ## expand RE and strip ~ ftemplate <- substitute(.~.-XX, list(XX=rr)) compReForm <- update.formula(compReForm,ftemplate)[-2] ## update, then delete LHS } ## (1) random effect(s) sim.reff <- if (!is.null(reformulas::findbars(compReForm))) { newRE <- mkNewReTrms(object, newdata, compReForm, na.action=na.action, allow.new.levels=allow.new.levels) ## this *can* justifiably happen, if we are using mkNewReTrms ## in the context of predicting/simulating with a non-trivial ## re.form ... ## paranoia ... ## stopifnot(!is.null(newdata) || ## isTRUE(all.equal(newRE$Lambdat,getME(object,"Lambdat")))) U <- t(newRE$Lambdat %*% newRE$Zt) # == Z Lambda u <- cluster.rand(ncol(U)*nsim) ## UNSCALED random-effects contribution: as(U %*% matrix(u, ncol = nsim), "matrix") } else 0 val <- if (isLMM(object)) { ## result will be matrix n x nsim : etapred + sigma * (sim.reff + ## residual contribution: if (cond.sim) # always rnorm regardless of cluster.rand matrix(rnorm(n * nsim), ncol = nsim) else 0) } else if (isGLMM(object)) { ## GLMM ## n.b. DON'T scale random-effects (???) etasim <- etapred+sim.reff family <- normalizeFamilyName(object@resp$family) musim <- family$linkinv(etasim) #-> family$family == "negative.binomial" if(NB) ## ntot <- length(musim) ## FIXME: or could be dims["n"]? ## if (family$family=="binomial" && is.matrix(r <- model.response(object@frame))) { # unless the user passed in new weights, take them from the response matrix # e.g. cbind(incidence, size-incidence) ~ ... if(nullWts) weights <- rowSums(r) } if (is.null(sfun <- simfunList[[family$family]])) { ## family$simulate just won't work ... ## sim funs must be hard-coded, see below stop("simulation not implemented for family ", sQuote(family$family)) } ## don't rely on automatic recycling if (cond.sim) { val <- sfun(object, nsim=1, ftd = rep_len(musim, n*nsim), wts = weights) } else { val <- rep_len(musim, n*nsim) } ## split results into nsims: need special case for binomial matrix/factor responses if (family$family=="binomial" && is.matrix(r <- model.response(object@frame))) { lapply(split(val[[1]], gl(nsim, n, 2 * nsim * n)), matrix, ncol = 2, dimnames = list(NULL, colnames(r))) } else if (family$family=="binomial" && is.factor(val[[1]])) { split(val[[1]], gl(nsim,n)) } else split(val, gl(nsim,n)) } else stop("simulate method for NLMMs not yet implemented") ## from src/library/stats/R/lm.R if(!is.list(val)) { dim(val) <- c(n, nsim) val <- as.data.frame(val) } else class(val) <- "data.frame" names(val) <- paste("sim", seq_len(nsim), sep="_") ## have not yet filled in NAs, so need to use names of fitted ## object NOT including values with NAs f <- fitted(object) nm <- names(f)[!is.na(f)] ## unnamed input, *or* simulation from new data ... if (length(nm) == 0) { nm <- as.character(seq(n)) } else if (!is.null(newdata)) { nm <- rownames(newdata) } row.names(val) <- nm fit.na.action <- attr(model.frame(object), "na.action") if (!missing(na.action) && !is.null(fit.na.action)) { ## retrieve name of na.action type ("omit", "exclude", "pass") class.na.action <- class(attr(na.action(NA), "na.action")) if (!identical(class.na.action, class(fit.na.action))) { ## hack to override action where explicitly specified class(fit.na.action) <- class.na.action } } nafun <- function(x) { x[] <- apply(x, 2L, napredict, omit = fit.na.action); x } val <- if (is.matrix(val[[1]])) { ## have to handle binomial response matrices differently -- ## fill in NAs as appropriate in *both* columns structure(lapply(val, nafun), ## have to put this back into a (weird) data frame again, ## carefully (should do the napredict stuff ## earlier, so we don't have to redo this transformation!) class = "data.frame") } else { as.data.frame(lapply(val, napredict, omit=fit.na.action)) } ## reconstruct names: first get rid of NAs, then refill them ## as appropriate based on fit.na.action (which may be different ## from the original model's na.action spec) nm2 <- if (is.null(newdata)) names(napredict(na.omit(f), omit=fit.na.action)) else rownames(napredict(newdata, omit=fit.na.action)) if (length(nm2) > 0) row.names(val) <- nm2 structure(val, ## as.data.frame(lapply(...)) blows away na.action attribute, ## so we have to re-assign here na.action = fit.na.action, seed = RNGstate) }## .simulateFun() ######################## ## modified from stats/family.R ## TODO: the $simulate methods included with R families by default ## are not sufficiently flexible to be re-used by lme4. ## these are modified by: ## (1) adding a 'ftd' argument for the fitted values ## that defaults to fitted(object), to allow more flexibility ## e.g. in conditioning on or marginalizing over random effects ## (fitted(object) can be produced from predict.merMod() with ## alternative parameters rather than being extracted directly ## from the fitted objects -- this allows simulation with new ## parameters or new predictor variables ## (2) modifying wts from object$prior.weights to weights(object) ## (3) adding wts as an argument ## ## these can be incorporated by overwriting the simulate() ## components, or calling them ## gaussian_simfun <- function(object, nsim, ftd=fitted(object), wts=weights(object)) { if (any(wts != 1)) warning("ignoring prior weights") rnorm(nsim*length(ftd), ftd, sd=sigma(object)) } binomial_simfun <- function(object, nsim, ftd=fitted(object), wts=weights(object)) { n <- length(ftd) ntot <- n*nsim if (any(wts %% 1 != 0)) stop("cannot simulate from non-integer prior.weights") ## Try to figure out if the original data were ## proportions, a factor or a two-column matrix if (!is.null(m <- model.frame(object))) { y <- model.response(m) if(is.factor(y)) { ## ignore weights yy <- factor(levels(y)[1 + rbinom(ntot, size = 1, prob = ftd)], levels = levels(y)) split(yy, rep(seq_len(nsim), each = n)) } else if(is.matrix(y) && ncol(y) == 2) { yy <- vector("list", nsim) for (i in seq_len(nsim)) { Y <- rbinom(n, size = wts, prob = ftd) YY <- cbind(Y, wts - Y) colnames(YY) <- colnames(y) yy[[i]] <- YY } yy } else rbinom(ntot, size = wts, prob = ftd)/wts } else rbinom(ntot, size = wts, prob = ftd)/wts } poisson_simfun <- function(object, nsim, ftd=fitted(object), wts=weights(object)) { ## A Poisson GLM has dispersion fixed at 1, so prior weights ## do not have a simple unambiguous interpretation: ## they might be frequency weights or indicate averages. wts <- weights(object) if (any(wts != 1)) warning("ignoring prior weights") rpois(nsim*length(ftd), ftd) } ##' FIXME: need a gamma.shape.merMod method in order for this to work. ##' (see initial shot at gamma.shape.merMod below) Gamma_simfun <- function(object, nsim, ftd=fitted(object), wts=weights(object)) { if (any(wts != 1)) message("using weights to scale shape parameter") ## used to use gamma.shape(), but sigma() is more general ## (wouldn't work *outside* of the merMod context though) shape <- 1/sigma(object)^2*wts rgamma(nsim*length(ftd), shape = shape, rate = shape/ftd) } gamma.shape.merMod <- function(object, ...) { if(family(object)$family != "Gamma") stop("Can not fit gamma shape parameter because Gamma family not used") y <- getME(object, "y") mu <- getME(object, "mu") w <- weights(object) # Sec 8.3.2 (MN) L <- w*(log(y/mu)-((y-mu)/mu)) dev <- -2*sum(L) # Eqs. between 8.2 & 8.3 (MN) Dbar <- dev/length(y) structure(list(alpha = (6+2*Dbar)/(Dbar*(6+Dbar)), SE = NA), # FIXME: obtain standard error class = "gamma.shape") } inverse.gaussian_simfun <- function(object, nsim, ftd=fitted(object), wts = weights(object)) { if (any(wts != 1)) message("using weights as inverse variances") if (!requireNamespace("statmod")) { stop("The ",sQuote("statmod")," package must be installed ", " in order to simulate inverse-Gaussian distributions") } statmod::rinvgauss(nsim * length(ftd), mean = ftd, shape= wts/sigma(object)) } ## in the original MASS version, .Theta is assigned into the environment ## (triggers a NOTE in R CMD check) ## modified from @aosmith16 GH contribution negative.binomial_simfun <- function (object, nsim, ftd = fitted(object), wts=weights(object)) { if (any(wts != 1)) warning("ignoring prior weights") theta <- getNBdisp(object) rnbinom(nsim * length(ftd), mu = ftd, size = theta) } simfunList <- list(gaussian = gaussian_simfun, binomial = binomial_simfun, poisson = poisson_simfun, Gamma = Gamma_simfun, negative.binomial = negative.binomial_simfun, inverse.gaussian = inverse.gaussian_simfun) lme4/R/error_factory.R0000644000176200001440000000615315022107260014317 0ustar liggesusers#' Catch errors and warnings and store them for subsequent evaluation #' #' Factory modified from a version written by Martin Morgan on Stack Overflow (see below). #' Factory generates a function which is appropriately wrapped by error handlers. #' If there are no errors and no warnings, the result is provided. #' If there are warnings but no errors, the result is provided with a warn attribute set. #' If there are errors, the result retutrns is a list with the elements of warn and err. #' This is a nice way to recover from a problems that may have occurred during loop evaluation or during cluster usage. #' Check the references for additional related functions. #' I have not included the other factory functions included in the original Stack Overflow answer because they did not play well with the return item as an S4 object. #' @export #' @param fun The function to be turned into a factory #' @param debug print debugging statements? #' @param errval the value to be returned from the function if an error is thrown #' @param types which types to catch? #' @return The result of the function given to turn into a factory. If this function was in error "An error as occurred" as a character element. factory-error and factory-warning attributes may also be set as appropriate. #' @references #' \url{http://stackoverflow.com/questions/4948361/how-do-i-save-warnings-and-errors-as-output-from-a-function} #' @author Martin Morgan; Modified by Russell S. Pierce and Ben Bolker #' @examples #' f.log <- factory(log) #' f.log("a") #' f.log.NA <- factory(log,errval=NA) #' f.log.NA("a") #' f.as.numeric <- factory(as.numeric) #' f.as.numeric(c("a","b",1)) factory <- function (fun, debug=FALSE, errval="An error occurred in the factory function", types=c("message","warning","error")) { function(...) { errorOccurred <- FALSE warn <- err <- msg <- NULL res <- withCallingHandlers(tryCatch(fun(...), error = function(e) { if (debug) cat("error: ",conditionMessage(e),"\n") err <<- conditionMessage(e) errorOccurred <<- TRUE NULL }), warning = function(w) { if (!"warning" %in% types) { warning(conditionMessage(w)) } else { warn <<- append(warn, conditionMessage(w)) invokeRestart("muffleWarning") } }, message = function(m) { if (debug) cat("message: ",conditionMessage(m),"\n") if (!"message" %in% types) { message(conditionMessage(m)) } else { msg <<- append(msg, conditionMessage(m)) invokeRestart("muffleMessage") } }) if (errorOccurred) { if (!"error" %in% types) stop(err) res <- errval } setattr <- function(x, attrib, value) { attr(x,attrib) <- value x } attr_fun <- function(x,str,msg) { setattr(x,paste0("factory-",str), if(is.character(msg)) msg else NULL) } res <- attr_fun(res, "message", msg) res <- attr_fun(res, "warning", warn) res <- attr_fun(res, "error", err) return(res) } } lme4/R/plots.R0000644000176200001440000001110215022107260012566 0ustar liggesusers### Plots for the ranef.mer class ---------------------------------------- ##' @importFrom lattice dotplot ##' @S3method dotplot ranef.mer dotplot.ranef.mer <- function(x, data, main = TRUE, transf=I, level = 0.95, ...) { rng <- qnorm((1+level)/2) prepanel.ci <- function(x, y, se, subscripts, ...) { if (is.null(se)) return(list()) x <- as.numeric(x) hw <- rng * as.numeric(se[subscripts]) list(xlim = range(transf(x - hw), transf(x + hw), finite = TRUE)) } panel.ci <- function(x, y, se, subscripts, pch = 16, horizontal = TRUE, col = dot.symbol$col, lty.h = dot.line$lty, lty.v = dot.line$lty, lwd.h = dot.line$lwd, lwd.v = dot.line$lwd, col.line.h = dot.line$col, col.line.v = dot.line$col, levels.fos = unique(y), groups = NULL, ...) { x <- as.numeric(x) y <- as.numeric(y) dot.line <- trellis.par.get("dot.line") dot.symbol <- trellis.par.get("dot.symbol") sup.symbol <- trellis.par.get("superpose.symbol") panel.abline(h = levels.fos, col = col.line.h, lty = lty.h, lwd = lwd.h) panel.abline(v = 0, col = col.line.v, lty = lty.v, lwd = lwd.v) if (!is.null(se)) { se <- as.numeric(se[subscripts]) panel.segments( transf(x - rng * se), y, transf(x + rng * se), y, col = 'black') } panel.xyplot(transf(x), y, pch = pch, col = col, ...) } f <- function(nx, ...) { ss <- asDf0(x,nx) mtit <- if(main) nx dotplot(.nn ~ values | ind, ss, se = ss$se, prepanel = prepanel.ci, panel = panel.ci, xlab = NULL, main = mtit, ...) } setNames(lapply(names(x), f, ...), names(x)) } ##' @importFrom graphics plot ##' @S3method plot ranef.mer plot.ranef.mer <- function(x, y, ...) { lapply(x, function(x) { cn <- lapply(colnames(x), as.name) switch(min(ncol(x), 3), qqmath(eval(substitute(~ x, list(x = cn[[1]]))), x, ...), xyplot(eval(substitute(y ~ x, list(y = cn[[1]], x = cn[[2]]))), x, ...), splom(~ x, ...)) }) } ##' @importFrom lattice qqmath ##' @S3method qqmath ranef.mer qqmath.ranef.mer <- function(x, data, main = TRUE, level = 0.95, ...) { rng <- qnorm((1+level)/2) prepanel.ci <- function(x, y, se, subscripts, ...) { x <- as.numeric(x) se <- as.numeric(se[subscripts]) hw <- rng * se list(xlim = range(x - hw, x + hw, finite = TRUE)) } panel.ci <- function(x, y, se, subscripts, pch = 16, ...) { panel.grid(h = -1,v = -1) panel.abline(v = 0) x <- as.numeric(x) y <- as.numeric(y) se <- as.numeric(se[subscripts]) panel.segments(x - rng * se, y, x + rng * se, y, col = 'black') panel.xyplot(x, y, pch = pch, ...) } f <- function(nx) { xt <- x[[nx]] mtit <- if(main) nx # else NULL if (!is.null(pv <- attr(xt, "postVar"))) { d <- dim(pv) se <- vapply(seq_len(d[1]), function(i) sqrt(pv[i, i, ]), numeric(d[3])) nr <- nrow(xt) nc <- ncol(xt) ord <- unlist(lapply(xt, order)) + rep((0:(nc - 1)) * nr, each = nr) rr <- 1:nr ind <- gl(nc, nr, labels = names(xt)) xyplot(rep(qnorm((rr - 0.5)/nr), nc) ~ unlist(xt)[ord] | ind[ord], se = se[ord], prepanel = prepanel.ci, panel = panel.ci, scales = list(x = list(relation = "free")), ylab = "Standard normal quantiles", xlab = NULL, main = mtit, ...) } else { qqmath(~values|ind, data = stack(xt), scales = list(y = list(relation = "free")), xlab = "Standard normal quantiles", ylab = NULL, main = mtit, ...) } } sapply(names(x), f, simplify = FALSE) } ##' @importFrom graphics plot ##' @S3method plot coef.mer plot.coef.mer <- function(x, y, ...) { ## remove non-varying columns from frames reduced <- lapply(x, function(el) el[, !vapply(el, function(cc) all(cc == cc[1L]), NA)]) plot.ranef.mer(reduced, ...) } ##' @importFrom lattice dotplot ##' @S3method dotplot coef.mer dotplot.coef.mer <- function(x, data, ...) { mc <- match.call() mc[[1]] <- as.name("dotplot.ranef.mer") eval(mc) } lme4/R/checkConv.R0000644000176200001440000002046615113136605013353 0ustar liggesusers### Adapted from Rune Haubo's ordinal code ### extended convergence checking ### http://en.wikipedia.org/wiki/Karush%E2%80%93Kuhn%E2%80%93Tucker_conditions ## global (for use in several functions) help_str <- "\n See ?lme4::convergence and ?lme4::troubleshooting." ##' @param derivs typically the "derivs" attribute of optimizeLmer(); with ##' "gradients" and possibly "Hessian" component ##' @param coefs estimated function value ##' @param ctrl list of lists, each with \code{action} character strings specifying ##' what should happen when a check triggers, and \code{tol} numerical tolerances, ##' as is the result of \code{\link{lmerControl}()$checkConv}. ##' @param lbound vector of lower bounds \emph{for random-effects parameters only} ##' (length is taken to determine number of RE parameters) ##' @param debug useful if some checks are on "ignore", but would "trigger" ##' @param nobs the number of observations from the dataset ##' @param ndim the number of dimensions for the variance-covariance matrix ##' of random effects checkConv <- function(derivs, coefs, ctrl, lbound, debug = FALSE, nobs = NULL, ndim = NULL) { res <- list() ntheta <- length(lbound) ## check singularity first, and unconditionally ## (ignore "ignore") ccl <- ctrl[[cstr <- "check.conv.singular"]] ; checkCtrlLevels(cstr, cc <- ccl[["action"]]) ## similar logic to isSingular, but we don't have the fitted object to test bcoefs <- seq(ntheta)[lbound==0] is.singular <- any(coefs[bcoefs] < ccl$tol) if (doCheck(cc)) { ## singular fit ## are there other circumstances where we can get a singular fit? if (is.singular) { wstr <- "boundary (singular) fit: see help('isSingular')" res$messages <- c(res$messages,wstr) switch(cc, "message" = message(wstr), "warning" = warning(wstr), "stop" = stop(wstr), stop(gettextf("unknown check level for '%s'", cstr), domain=NA)) } } ## DON'T check remaining gradient issues if (is.singular) return(res) ## bail out if (is.null(derivs) || (!is.null(nobs) && nobs > ctrl$check.conv.nobsmax) || (!is.null(ndim) && ndim > ctrl$check.conv.nparmax)) { return(NULL) } if (anyNA(derivs$gradient)) return(list(code = -5L, messages = gettextf("Gradient contains NAs"))) ## gradients: ## check absolute gradient (default) ccl <- ctrl[[cstr <- "check.conv.grad"]] ; checkCtrlLevels(cstr, cc <- ccl[["action"]]) wstr <- NULL if (doCheck(cc)) { scgrad <- tryCatch(with(derivs,solve(chol(Hessian),gradient)), error=function(e)e) if (inherits(scgrad, "error") || ## some BLAS versions return NA rather than throwing an error? ## GH #677 any(is.na(scgrad))) { wstr <- "unable to evaluate scaled gradient" res$code <- -1L } else { ## find parallel *minimum* of scaled and absolute gradient ## the logic here is that we can sometimes get large ## *scaled* gradients even when the *absolute* gradient ## is small because the curvature is very flat as well ... mingrad <- pmin(abs(scgrad),abs(derivs$gradient)) maxmingrad <- max(mingrad) if (maxmingrad > ccl$tol) { w <- which.max(maxmingrad) res$code <- -1L wstr <- gettextf("Model failed to converge with max|grad| = %g (tol = %g, component %d)", maxmingrad, ccl$tol,w) wstr <- paste0(wstr, help_str) } } if (!is.null(wstr)) { res$messages <- wstr switch(cc, "warning" = warning(wstr), "stop" = stop(wstr), stop(gettextf("unknown check level for '%s'", cstr), domain=NA)) } ## note: kktc package uses gmax > kkttol * (1 + abs(fval)) ## where kkttol defaults to 1e-3 and fval is the objective f'n value ## check relative gradient (only if enabled) if (!is.null(ccl$relTol) && (max.rel.grad <- max(abs(derivs$gradient/coefs))) > ccl$relTol) { res$code <- -2L wstr <- gettextf("Model failed to converge with max|relative grad| = %g (tol = %g)", max.rel.grad, ccl$relTol) wstr <- paste0(wstr, help_str) res$messages <- wstr switch(cc, "warning" = warning(wstr), "stop" = stop(wstr), stop(gettextf("unknown check level for '%s'", cstr), domain=NA)) } } ccl <- ctrl[[cstr <- "check.conv.hess"]] ; checkCtrlLevels(cstr, cc <- ccl[["action"]]) if (doCheck(cc)) { if (length(coefs) > ntheta) { ## GLMM, check for issues with beta parameters H.beta <- derivs$Hessian[-seq(ntheta),-seq(ntheta)] resHess <- checkHess(H.beta, ccl$tol, "fixed-effect") if (any(resHess$code!=0)) { res$code <- resHess$code res$messages <- c(res$messages,resHess$messages) wstr <- paste(resHess$messages,collapse=";") switch(cc, "warning" = warning(wstr), "stop" = stop(wstr), stop(gettextf("unknown check level for '%s'", cstr), domain=NA)) } } resHess <- checkHess(derivs$Hessian, ccl$tol) if (any(resHess$code != 0)) { res$code <- resHess$code res$messages <- c(res$messages,resHess$messages) wstr <- paste(resHess$messages,collapse=";") switch(cc, "warning" = warning(wstr), "stop" = stop(wstr), stop(gettextf("unknown check level for '%s'", cstr), domain=NA)) } } if (debug && length(res$messages) > 0) { print(res$messages) } res } checkHess <- function(H, tol, hesstype="") { ## FIXME: not sure why we decided to save messages as a list ## rather than as a character vector?? res <- list(code=numeric(0),messages=list()) evd <- tryCatch(eigen(H, symmetric=TRUE, only.values=TRUE)$values, error=function(e)e) if (inherits(evd,"error")) { res$code <- -6L res$messages <- gettextf("Problem with Hessian check (infinite or missing values?)") } else { negative <- sum(evd < -tol) if(negative) { res$code <- -3L res$messages <- gettextf(paste("Model failed to converge:", "degenerate",hesstype,"Hessian with %d negative eigenvalues"), negative) res$messages <- paste0(res$messages, help_str) } else { zero <- sum(abs(evd) < tol) if(zero || inherits(tryCatch(chol(H), error=function(e)e), "error")) { res$code <- -4L res$messages <- paste(hesstype,"Hessian is numerically singular: parameters are not uniquely determined") } else { res$cond.H <- max(evd) / min(evd) if(max(evd) * tol > 1) { res$code <- c(res$code, 2L) res$messages <- c(res$messages, paste("Model is nearly unidentifiable: ", "very large eigenvalue", "\n - Rescale variables?", sep="")) } if ((min(evd) / max(evd)) < tol) { res$code <- c(res$code, 3L) ## consider skipping warning message if we've ## already hit the previous flag? if(!5L %in% res$code) { res$messages <- c(res$messages, paste("Model is nearly unidentifiable: ", "large eigenvalue ratio", "\n - Rescale variables?", sep="")) } } } } } if (length(res$code)==0) res$code <- 0 res } lme4/LICENSE.note0000644000176200001440000000022615022107260013057 0ustar liggesusersThis package is licensed under GPL (>=2), except for the code in R/simulate.formula.R, which is licensed under the MIT license (details in that file).lme4/vignettes/0000755000176200001440000000000015113144725013126 5ustar liggesuserslme4/vignettes/lmer.bib0000644000176200001440000004257115022107260014544 0ustar liggesusers%% This BibTeX bibliography file was created using BibDesk. %% http://bibdesk.sourceforge.net/ %% Created for Steven Walker at 2014-02-14 16:07:20 -0500 %% Saved with string encoding Unicode (UTF-8) @InCollection{Chambers:1993, author = {John M. Chambers}, title = {Linear Models}, booktitle = {Statistical Models in \proglang{S}}, publisher = {Chapman \& Hall}, year = 1993, editor = {John M. Chambers and Trevor J. Hastie}, chapter = 4, pages = {95--144}, } @book{Rauden:Bryk:2002, Author = {Stephen W. Raudenbush and Anthony S. Bryk}, Edition = {2nd}, Isbn = {0-7619-1904-X}, Publisher = {Sage}, Title = {Hierarchical Linear Models: Applications and Data Analysis Methods}, Year = 2002 } @book{MLwiNUser:2000, Address = {London}, Author = {J. Rasbash and W. Browne and H. Goldstein and M. Yang and I. Plewis}, Publisher = {Multilevel Models Project, Institute of Education, University of London}, Title = {A User's Guide to \pkg{MLwiN}}, Year = 2000} @Book{davis06:csparse_book, address = {Philadelphia, PA}, author = {Tim Davis}, publisher = {SIAM}, title = {Direct Methods for Sparse Linear Systems}, year = 2006, doi = {10.1137/1.9780898718881}, } @Article{laird_ware_1982, author = {Nan M. Laird and James H. Ware}, journal = {Biometrics}, pages = {963--974}, title = {Random-Effects Models for Longitudinal Data}, volume = 38, number = 4, year = 1982, doi = {10.2307/2529876}, } @Book{bateswatts88:_nonlin, address = {Hoboken, NJ}, author = {Douglas M. Bates and Donald G. Watts}, publisher = {John Wiley \& Sons}, title = {Nonlinear Regression Analysis and Its Applications}, year = 1988, doi = {10.1002/9780470316757}, } @book{R:Pinheiro+Bates:2000, Author = {Jose C. Pinheiro and Douglas M. Bates}, Title = {Mixed-Effects Models in \proglang{S} and \proglang{S-PLUS}}, Year = 2000, Orderinfo = {springer.txt}, ISBN = {0-387-98957-0}, Publisher = {Springer-Verlag}, Abstract = {A comprehensive guide to the use of the `nlme' package for linear and nonlinear mixed-effects models.}, } @article{bates04:_linear, Author = {Douglas M. Bates and Saikat DebRoy}, Journal = {Journal of Multivariate Analysis}, doi = {10.1016/j.jmva.2004.04.013}, Number = 1, Pages = {1--17}, Title = {Linear Mixed Models and Penalized Least Squares}, Volume = 91, Year = 2004} @article{gelman2005analysis, title = {Analysis of Variance --- Why it is More Important than Ever}, author = {Gelman, Andrew}, journal = {The Annals of Statistics}, volume = 33, number = 1, pages = {1--53}, year = 2005, doi = {10.1214/009053604000001048}, publisher = {Institute of Mathematical Statistics} } @ARTICLE{1977EfronAndMorris, author = {{Efron}, B. and {Morris}, C.}, title = "{Stein's Paradox in Statistics}", journal = {Scientific American}, year = 1977, month = may, volume = 236, pages = {119-127}, doi = {10.1038/scientificamerican0577-119}, adsurl = {http://adsabs.harvard.edu/abs/1977SciAm.236e.119E}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } @article{henderson_1982, author = {Charles R. {Henderson Jr.}}, Title = {Analysis of Covariance in the Mixed Model: Higher-Level, Nonhomogeneous, and Random Regressions}, Journal = {Biometrics}, Year = 1982, Volume = 38, Number = 3, Pages = {623--640}, Language = {English}, Publisher = {International Biometric Society}, Url = {http://www.jstor.org/stable/2530044}, } @article{golub_pereyra_1973, Author = {Golub, G. H. and Pereyra, V.}, Title = {The Differentiation of Pseudo-Inverses and Nonlinear Least Squares Problems Whose Variables Separate}, Year = 1973, Journal = {SIAM Journal on Numerical Analysis}, Volume = 10, Number = 2, Pages = {413--432}, doi = {10.1137/0710036}, } @article{sleepstudy, Author = {Gregory Belenky and Nancy J. Wesensten and David R. Thorne and Maria L. Thomas and Helen C. Sing and Daniel P. Redmond and Michael B. Russo and Thomas J. Balkin}, Date-Modified = {2014-02-14 21:07:17 +0000}, Journal = {Journal of Sleep Research}, Pages = {1--12}, Title = {Patterns of Performance Degradation and Restoration During Sleep Restriction and Subsequent Recovery: A Sleep Dose-Response Study}, Volume = 12, Year = 2003, doi = {10.1046/j.1365-2869.2003.00337.x}, } @article{Chen:2008:ACS:1391989.1391995, author = {Chen, Yanqing and Davis, Timothy A. and Hager, William W. and Rajamanickam, Sivasankaran}, title = {Algorithm 887: CHOLMOD, Supernodal Sparse Cholesky Factorization and Update/Downdate}, journal = {ACM Trans. Math. Softw.}, year = 2008, volume = 35, number = 3, month = oct, issn = {0098-3500}, pages = {22:1--22:14}, articleno = 22, doi = {10.1145/1391989.1391995}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Cholesky factorization, linear equations, sparse matrices}, } @article{kenward_small_1997, author = {M. G Kenward and J. H Roger}, title = {Small Sample Inference for Fixed Effects from Restricted Maximum Likelihood}, year = {1997}, journal = {Biometrics}, volume = {53}, number = {3}, pages = {983--997}, abstract = {Restricted maximum likelihood {(REML)} is now well established as a method for estimating the parameters of the general Gaussian linear model with a structured covariance matrix, in particular for mixed linear models. Conventionally, estimates of precision and inference for fixed effects are based on their asymptotic distribution, which is known to be inadequate for some small-sample problems. In this paper, we present a scaled Wald statistic, together with an F approximation to its sampling distribution, that is shown to perform well in a range of small sample settings. The statistic uses an adjusted estimator of the covariance matrix that has reduced small sample bias. This approach has the advantage that it reproduces both the statistics and F distributions in those settings where the latter is exact, namely for Hotelling T\${\textasciicircum}2\$ type statistics and for analysis of variance F-ratios. The performance of the modified statistics is assessed through simulation studies of four different {REML} analyses and the methods are illustrated using three examples.}, doi = {10.2307/2533558}, } @Article{Satterthwaite_1946, author = {F. E. Satterthwaite}, title = {An Approximate Distribution of Estimates of Variance Components}, journal = {Biometrics Bulletin}, year = 1946, volume = 2, number = 6, pages = {110-114}, doi = {10.2307/3002019}, } @Manual{gamm4, title = {\pkg{gamm4}: Generalized Additive Mixed Models Using \pkg{mgcv} and \pkg{lme4}}, author = {Simon Wood and Fabian Scheipl}, year = 2014, note = {\proglang{R} package version 0.2-3}, url = {http://CRAN.R-project.org/package=gamm4}, } @Manual{blme, title = {\pkg{blme}: {Bayesian} Linear Mixed-Effects Models}, author = {Vincent Dorie}, year = 2015, note = {R package version 1.0-4}, url = {http://CRAN.R-project.org/package=blme}, } @Article{blme2, title = {A Nondegenerate Penalized Likelihood Estimator for Variance Parameters in Multilevel Models}, author = {Yeojin Chung and Sophia Rabe-Hesketh and Vincent Dorie and Andrew Gelman and Jingchen Liu}, year = 2013, journal = {Psychometrika}, volume = 78, number = 4, pages = {685--709}, } @article{doran2007estimating, author={Doran, Harold and Bates, Douglas and Bliese, Paul and Dowling, Maritza}, title={Estimating the Multilevel {Rasch} Model: With the \pkg{lme4} Package}, year=2007, journal={Journal of Statistical Software}, volume=20, number=2, pages={1--18}, doi = {10.18637/jss.v020.i02}, publisher={American Statistical Association}, } @TechReport{Powell_bobyqa, author = {M. J. D. Powell}, title = {The {BOBYQA} Algorithm for Bound Constrained Optimization without Derivatives}, institution = {Centre for Mathematical Sciences, University of Cambridge}, year = {2009}, number = {DAMTP 2009/NA06}, address = {Cambridge, England}, url = {http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2009_06.pdf} } @article{pinheiro_unconstrained_1996, title = {Unconstrained Parametrizations for Variance-Covariance Matrices}, volume = {6}, doi = {10.1007/BF00140873}, abstract = {The estimation of variance-covariance matrices through optimization of an objective function, such as a log-likelihood function, is usually a difficult numerical problem. Since the estimates should be positive semi-definite matrices, we must use constrained optimization, or employ a parametrization that enforces this condition. We describe here five different parametrizations for variance-covariance matrices that ensure positive definiteness, thus leaving the estimation problem unconstrained. We compare the parametrizations based on their computational efficiency and statistical interpretability. The results described here are particularly useful in maximum likelihood and restricted maximum likelihood estimation in linear and non-linear mixed-effects models, but are also applicable to other areas of statistics.}, number = {3}, urldate = {2010-01-05}, journal = {Statistics and Computing}, author = {Pinheiro, JosĂ© C. and Bates, Douglas M.}, year = {1996}, pages = {289--296} } @article{bolker_strategies_2013, title = {Strategies for Fitting Nonlinear Ecological Models in \proglang{R}, \pkg{AD Model Builder}, and \proglang{BUGS}}, volume = 4, doi = {10.1111/2041-210X.12044}, number = 6, urldate = {2013-06-11}, journal = {Methods in Ecology and Evolution}, author = {Bolker, Benjamin M. and Gardner, Beth and Maunder, Mark and Berg, Casper W. and Brooks, Mollie and Comita, Liza and Crone, Elizabeth and Cubaynes, Sarah and Davies, Trevor and de Valpine, Perry and Ford, Jessica and Gimenez, Olivier and KĂ©ry, Marc and Kim, Eun Jung and Lennert-Cody, Cleridy and Magnusson, Arni and Martell, Steve and Nash, John and Nielsen, Anders and Regetz, Jim and Skaug, Hans and Zipkin, Elise}, editor = {Ramula, Satu}, month = jun, year = 2013, pages = {501--512}, pdf={bbpapers/bolker_strategies_2013.pdf} } @Article{Klein_nelder_2013, author = {Kyle Klein and Julian Neira}, title = {{Nelder-Mead} Simplex Optimization Routine for Large-Scale Problems: A Distributed Memory Implementation}, journal = {Computational Economics}, year = 2013, doi = {10.1007/s10614-013-9377-8} } @Misc{merBoot, author = {JosĂ© A. Sánchez-Espigares and Jordi Ocaña}, title = {An \proglang{R} Implementation of Bootstrap Procedures for Mixed Models}, howpublished = {Conference presentation, useR!}, month = {July}, year = {2009}, note = {accessed 25 May 2014}, url = {http://www.r-project.org/conferences/useR-2009/slides/SanchezEspigares+Ocana.pdf} } @book{gelman_data_2006, address = {Cambridge, England}, title = {Data Analysis Using Regression and {Multilevel/Hierarchical} Models}, url = {http://www.stat.columbia.edu/~gelman/arm/}, publisher = {Cambridge University Press}, author = {Gelman, Andrew and Hill, Jennifer}, year = {2006}, keywords = {uploaded} } @article{khatri1968solutions, title={Solutions to Some Functional Equations and their Applications to Characterization of Probability Distributions}, author={Khatri, CG and Rao, C Radhakrishna}, journal={Sankhy{\=a}: The Indian Journal of Statistics A}, pages={167--180}, volume = 30, number = 2, year = 1968, } @incollection{zhang2006schur, year={2005}, booktitle={The Schur Complement and its Applications}, volume={4}, series={Numerical Methods and Algorithms}, editor={Zhang, Fuzhen}, title={Basic Properties of the Schur Complement}, doi = {10.1007/0-387-24273-2_2}, publisher={Springer-Verlag}, author={Horn, RogerA. and Zhang, Fuzhen}, pages={17--46} } @book{gelman2013bayesian, title={Bayesian Data Analysis}, author={Gelman, Andrew and Carlin, John B and Stern, Hal S and Dunson, David B and Vehtari, Aki and Rubin, Donald B}, year={2013}, publisher={CRC press} } @Manual{Matrix_pkg, title = {\pkg{Matrix}: Sparse and Dense Matrix Classes and Methods}, author = {Douglas Bates and Martin Maechler}, year = 2015, note = {\proglang{R} package version 1.2-2}, url = {http://CRAN.R-project.org/package=Matrix}, } @Manual{minqa_pkg, title = {\pkg{minqa}: Derivative-Free Optimization Algorithms by Quadratic Approximation}, author = {Douglas Bates and Katharine M. Mullen and John C. Nash and Ravi Varadhan}, year = {2014}, note = {\proglang{R} package version 1.2.4}, url = {http://CRAN.R-project.org/package=minqa}, } @Article{optimx_pkg, author = {John C. Nash and Ravi Varadhan}, title = {Unifying Optimization Algorithms to Aid Software System Users: \pkg{optimx} for \proglang{R}}, journal = {Journal of Statistical Software}, year = 2011, volume = 43, number = 9, pages = {1--14}, doi = {10.18637/jss.v043.i09}, url = {http://www.jstatsoft.org/v43/i09/}, } @Misc{NLopt, author = {Steven G. Johnson}, title = {The \pkg{NLopt} Nonlinear-Optimization Package}, year = {2014}, url = {http://ab-initio.mit.edu/nlopt} } @Manual{nlme_pkg, title = {\pkg{nlme}: Linear and Nonlinear Mixed Effects Models}, author = {Jose Pinheiro and Douglas Bates and Saikat DebRoy and Deepayan Sarkar and {\proglang{R} Core Team}}, year = 2014, note = {\proglang{R} package version 3.1-117}, url = {http://CRAN.R-project.org/package=nlme}, } @Article{HLMdiag_pkg, title = {\pkg{HLMdiag}: A Suite of Diagnostics for Hierarchical Linear Models in \proglang{R}}, author = {Adam Loy and Heike Hofmann}, journal = {Journal of Statistical Software}, year = 2014, volume = 56, number = 5, pages = {1--28}, doi = {10.18637/jss.v056.i05}, url = {http://www.jstatsoft.org/v56/i05/}, } @Article{influenceME_pkg, title = {\proglang{Influence.ME}: Tools for Detecting Influential Data in Mixed Effects Models}, author = {Rense Nieuwenhuis and Manfred {Te Grotenhuis} and Ben Pelzer}, year = 2012, journal = {R Journal}, volume = 4, number = 2, pages = {38-47}, } @Manual{boot_pkg, author = {Angelo Canty and Brian Ripley}, year = 2015, title = { \pkg{boot}: Bootstrap \proglang{R} (\proglang{S-PLUS}) Functions}, note = {\proglang{R} package version 1.3-17}, url = {http://CRAN.R-project.org/package=boot} } @Book{DavisonHinkley1997, author = {A. C. Davison and D. V. Hinkley}, title = {Bootstrap Methods and Their Applications}, year = 1997, publisher = {Cambridge University Press}, ISBN = {0-521-57391-2}, address = {Cambridge, England} } @article{vaida2005conditional, title={Conditional Akaike Information for Mixed-Effects Models}, author={Vaida, Florin and Blanchard, Suzette}, journal={Biometrika}, volume={92}, number={2}, pages={351--370}, year={2005}, doi = {10.1093/biomet/92.2.351}, publisher={Biometrika Trust} } @book{cook1982residuals, title={Residuals and Influence in Regression}, author={Cook, R Dennis and Weisberg, Sanford}, year={1982}, publisher={New York: Chapman and Hall} } @comment -------- Software manuals, mostly R and R packages --------- @Manual{lme4, title = {\pkg{lme4}: Linear Mixed-Effects Models Using \pkg{Eigen} and \proglang{S}4}, author = {Douglas Bates and Martin Maechler and Ben Bolker and Steven Walker}, year = 2014, note = {\proglang{R} package version 1.1-7}, url = {http://CRAN.R-project.org/package=lme4}, } @Manual{R, title = {\proglang{R}: A Language and Environment for Statistical Computing}, author = {{\proglang{R} Core Team}}, organization = {\proglang{R} Foundation for Statistical Computing}, address = {Vienna, Austria}, year = 2015, url = {http://www.R-project.org/}, } @Unpublished{Julia, title = {\proglang{Julia}: A Fast Dynamic Language for Technical Computing}, author = {Jeff Bezanson and Stefan Karpinski and Viral B. Shah and Alan Edelman}, year = 2012, url = {http://arxiv.org/abs/1209.5145}, note = {{arXiv}:1209.5145 [cs.PL]}, } @Manual{MixedModels, title = {\pkg{MixedModels}: A \proglang{Julia} Package for Fitting (Statistical) Mixed-Effects Models}, author = {Douglas Bates}, year = 2015, note = {\proglang{Julia} package version 0.3-22}, url = {https://github.com/dmbates/MixedModels.jl}, } @Manual{lme4pureR, title = {\pkg{lme4pureR}: \pkg{lme4} in Pure \proglang{R}}, author = {Douglas Bates and Steven Walker}, year = 2013, note = {\proglang{R} package version 0.1-0}, url = {https://github.com/lme4/lme4pureR}, } @Manual{Eigen, title = {\pkg{Eigen}3}, author = {G Guennebaud and B Jacob and {and others}}, year = 2015, url = {http://eigen.tuxfamily.org/}, } @Manual{SuiteSparse, title = {\pkg{SuiteSparse}: A Suite of Sparse Matrix Software}, author = {Timothy A. Davis and others}, year = 2015, note = {Version 4.4-5}, url = {http://www.suitesparse.com/}, } @Book{lattice, title = {\pkg{lattice}: Multivariate Data Visualization with \proglang{R}}, author = {Deepayan Sarkar}, publisher = {Springer-Verlag}, address = {New York}, year = 2008, url = {http://lmdvr.R-Forge.R-project.org}, } lme4/vignettes/lme4.bib0000644000176200001440000000611615022107260014441 0ustar liggesusers@Book{bateswatts88:_nonlin, author = {Douglas M. Bates and Donald G. Watts}, title = {Nonlinear Regression Analysis and Its Applications}, publisher = {Wiley}, year = 1988} @Article{Davis:1996, author = {Tim Davis}, title = {An approximate minimal degree ordering algorithm}, journal = {SIAM J. Matrix Analysis and Applications}, year = 1996, volume = 17, number = 4, pages = {886-905} } @Misc{CSparse, author = {Tim Davis}, title = {{CSparse}: a concise sparse matrix package}, howpublished = {http://www.cise.ufl.edu/research/sparse/CSparse}, year = 2005 } @misc{Cholmod, author = {Tim Davis}, title = {{CHOLMOD}: sparse supernodal {Cholesky} factorization and update/downdate}, howpublished = {http://www.cise.ufl.edu/research/sparse/cholmod}, year = 2005 } @Book{mccullagh89:_gener_linear_model, author = {Peter McCullagh and John Nelder}, title = {Generalized Linear Models}, publisher = {Chapman and Hall}, year = 1989, edition = {2nd}} @Article{mccullough99:_asses_reliab_of_statis_softw, author = {B. D. McCullough}, title = {Assessing the reliability of statistical software: Part II}, journal = {The American Statistician}, year = 1999, volume = 53, number = 2, month = {May}} @Book{davis06:csparse_book, author = {Timothy A. Davis }, title = {Direct Methods for Sparse Linear Systems}, publisher = {SIAM}, year = 2006, series = {Fundamentals of Algorithms} } @Book{pinh:bate:2000, author = {Jos\'{e} C. Pinheiro and Douglas M. Bates}, title = {Mixed-Effects Models in {S} and {S-PLUS}}, year = 2000, pages = {528}, ISBN = {0-387-98957-9}, publisher = {Springer} } @Article{bate:debr:2004, author = {Douglas M. Bates and Saikat DebRoy}, title = {Linear Mixed Models and Penalized Least Squares}, journal = {J. of Multivariate Analysis}, year = 2004, note = {to appear} } @Book{mccullagh:nelder:1989, author = {P. McCullagh and J.A. Nelder}, title = {Generalized Linear Models}, publisher = {Chapman \& Hall}, year = 1989 } @TechReport{Davis:2004, author = {Timothy A. Davis}, title = {Algorithm 8xx: {A} concise sparse {C}holesky factorization package}, institution = {Department of Computer and Information Science and Engineering, University of Florida}, year = 2004 } @Article{tier:kada:1986, journal = JASA, volume = "81", number = "393", pages = "82--86", author = "Luke Tierney and Joseph B. Kadane", title = "Accurate approximations for posterior moments and densities", year = "1986", } @Book{Sing:Will:2003, author = {Judith D. Singer and John B. Willett}, title = {Applied Longitudinal Data Analysis}, publisher = {Oxford University Press}, year = 2003, ISBN = {0-19-515296-4} } @BOOK{R:Chambers+Hastie:1992, author = {John M. Chambers and Trevor J. Hastie}, title = {Statistical Models in {S}}, publisher = {Chapman \& Hall}, year = 1992, address = {London} } lme4/vignettes/downstream_methods.html0000644000176200001440000177217515032566716017756 0ustar liggesusers Downstream methods

Downstream methods

Many packages provide tools for downstream processing — e.g. plot diagnostics, model comparisons, visual plotting, and regression tables – that are compatible with merMod objects. This vignette provides instructions on some recommended packages with examples. Rather than attempting an exhaustive survey, We aimed to include the more popular packages.

Diagnostic Plots

While plot(merMod_object) provides some diagnostic plots (see ?lme4::plot.merMod), other packages provide more complete functionality.

This section emphasizes the performance and DHARMa package.

library(lme4)
# Example of a linear mixed-effects model (LMM)
mod_ss <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
# Assume a non-mixed model for comparison
mod_ss2 <- lm(Reaction ~ Days, sleepstudy)

# Another (LMM) example, and others for comparison
mod_cw = lmer(weight ~ Time + Diet + (1|Chick), data = ChickWeight, REML = FALSE)
mod_cw2 = lm(weight ~ Time + Diet, data = ChickWeight)
mod_cw3 = lmer(weight ~ Time + (1|Chick), data = ChickWeight, REML = FALSE)

# Example of a Poisson generalized linear mixed-effects model (GLMM)
form <- TICKS~YEAR+HEIGHT+(1|BROOD)+(1|INDEX)+(1|LOCATION)
mod_gt  <- glmer(form, family="poisson",data=grouseticks)

Performance

The performance package is excellent for evaluating the quality of model fit and works well with merMod objects (and many others). The first example we’ll show is performance::check_model(), which does checks for classical assumptions used for most linear models: normality of residuals, linear relationship, homogeneity of variance, outliers.

For mixed-effect models, it also checks for normality of random effects. The uncommonly used posterior predictive checks is meant to see whether the distributional family used fits well to the data.

performance::check_model(mod_ss)

For those working with mixed models, it may important to check whether some dimensions of the variance-covariance are estimated to be zero.

performance::check_singularity(mod_ss)
## [1] FALSE

DHARMa

The DHARMa package uses a simulation-based method for residual checks for generalized linear (mixed) models.

simulationOutput <- DHARMa::simulateResiduals(fittedModel = mod_ss)
plot(simulationOutput)

The left plot performs three tests, the Kolmogorov–Smirnov test, simulation-based dispersion tests (see ??DHARMa::testDispersion), and simulation-based outlier tests (see ??DHARMa::testOutliers).

Car

The car package was designed for “An R Companion to Applied Regression” to provide functions that are applied to a fitted regression model. The car::influencePlot() creates a diagnostic bubble plot that visualizes influential observations in a regression by plotting Studentized residuals against leverage (hat values), with bubble size representing Cook’s distance.

car::influencePlot(mod_ss)

##       StudRes       Hat     CookD
## 10  1.6589487 0.2931772 0.5707626
## 20  0.4498749 0.2931772 0.0419733
## 57  5.5270053 0.1218763 2.1198891
## 60 -4.5704359 0.2931772 4.3321637

Model Comparison & Performance

Most model comparisons for merMod objects will focus on the fixed effects, and the ones shown will be just for fixed effects unless otherwise stated.

Performance

performance::model_performance(mod_ss)
## # Indices of model performance
## 
## AIC      |     AICc |      BIC | R2 (cond.) | R2 (marg.) |   ICC |   RMSE |  Sigma
## ----------------------------------------------------------------------------------
## 1755.628 | 1756.114 | 1774.786 |      0.799 |      0.279 | 0.722 | 23.438 | 25.592

We can also use performance::compare_performance().

performance::compare_performance(mod_ss, mod_ss2)
## # Comparison of Model Performance Indices
## 
## Name    |   Model |  AIC (weights) | AICc (weights) |  BIC (weights) |   RMSE |  Sigma | R2 (cond.)
## ---------------------------------------------------------------------------------------------------
## mod_ss  | lmerMod | 1764.0 (>.999) | 1764.5 (>.999) | 1783.1 (>.999) | 23.438 | 25.592 |      0.799
## mod_ss2 |      lm | 1906.3 (<.001) | 1906.4 (<.001) | 1915.9 (<.001) | 47.449 | 47.715 |           
## 
## Name    | R2 (marg.) |   ICC |    R2 | R2 (adj.)
## ------------------------------------------------
## mod_ss  |      0.279 | 0.722 |       |          
## mod_ss2 |            |       | 0.286 |     0.282

Pbkrtest

The pbkrtest package implements three tests to examine fixed effects from linear mixed-effects models (LMM): - Parametric bootstrap test pbkrtest::PBmodcomp() - Kenward-Roger-type F-test pbkrtest::KRmodcomp() - Satterthwaite-type F-test pbkrtest::SATmodcomp()

fm0 <- lmer(weight ~ Time + (1|Chick), data = ChickWeight)
fm1 <- update(fm0, .~.-Time)

pbkrtest::PBmodcomp(fm0, fm1)
## Bootstrap test; time: 21.32 sec; samples: 1000; extremes: 0;
## large : weight ~ Time + (1 | Chick)
##          stat df   p.value    
## LRT    926.47  1 < 2.2e-16 ***
## PBtest 926.47     0.000999 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pbkrtest::KRmodcomp(fm0, fm1)
## large : weight ~ Time + (1 | Chick)
## small : weight ~ (1 | Chick)
##          stat     ndf     ddf F.scaling   p.value    
## Ftest 2471.07    1.00  530.53         1 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pbkrtest::SATmodcomp(fm0, fm1)
## large : weight ~ Time + (1 | Chick)
## small : weight ~ (1 | Chick)
##      statistic    ndf    ddf   p.value    
## [1,]    2471.7    1.0 530.93 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Note: the pbkrtest::KRmodcomp() function was package was intended for lmerMod objects only, as the Kenward-Roger-type F-test was designed for fitted linear mixed models.

MuMIn

The MuMIn package provides tools for model selection by automating the process through subsets of the maximum model.

options(na.action = "na.fail")

mumin_compare = MuMIn::dredge(mod_cw)
mumin_compare
## Global model call: lmer(formula = weight ~ Time + Diet + (1 | Chick), data = ChickWeight, 
##     REML = FALSE)
## ---
## Model selection table 
##   (Intrc) Diet  Time df    logLik   AICc  delta weight
## 4   11.23    + 8.718  7 -2802.600 5619.4   0.00  0.996
## 3   27.84      8.726  4 -2811.172 5630.4  11.02  0.004
## 2  101.80    +        6 -3265.144 6542.4 923.04  0.000
## 1  121.00             3 -3274.405 6554.9 935.46  0.000
## Models ranked by AICc(x) 
## Random terms (all models): 
##   1 | Chick

MuMIn::dredge() doesn’t print the fitted model object, so to find which the labelled models 1, 2, etc., correspond to, use get.models(mumin_compare, subset = TRUE). The output tends to be long and is omitted for space reasons.

RLRsim

A common way to test the significance of random effects is through a likelihood ratio test. The RLRsim package includes fast simulation-based exact tests that is compatible with lmerMod objects.

# First model is one with the random effects,
# the second is must be a lm-object.
RLRsim::exactLRT(m = mod_cw, m0 = mod_cw2)
## No restrictions on fixed effects. REML-based inference preferable.
## 
##  simulated finite sample distribution of LRT. (p-value based on 10000 simulated values)
## 
## data:  
## LRT = 172.41, p-value < 2.2e-16

Multcomp

The multcomp package was designed for performing general linear hypotheses in parametric models.

glh_cw = multcomp::glht(mod_cw)
summary(glh_cw)
## 
##   Simultaneous Tests for General Linear Hypotheses
## 
## Fit: lmer(formula = weight ~ Time + Diet + (1 | Chick), data = ChickWeight, 
##     REML = FALSE)
## 
## Linear Hypotheses:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept) == 0  11.2311     5.5780   2.013  0.17828    
## Time == 0          8.7175     0.1753  49.742  < 0.001 ***
## Diet2 == 0        16.2193     9.0788   1.787  0.28082    
## Diet3 == 0        36.5527     9.0788   4.026  < 0.001 ***
## Diet4 == 0        30.0255     9.0855   3.305  0.00447 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)

We can also look at confidence intervals for the pairwise difference between groups:

plot(glh_cw)

Car

car::Anova() can be used traditional ANOVA-style tables using Wald \(\chi^{2}\) statistics on the fixed effects only.

car::Anova(mod_cw)
## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: weight
##         Chisq Df Pr(>Chisq)    
## Time 2474.247  1  < 2.2e-16 ***
## Diet   20.466  3  0.0001359 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Insight

As its name suggests, the insight package was designed to easily gather information from statistical models from a variety of different object types. Their documentation already covers examples of merMod objects here.

Plotting & Prediction

When interpreting results, we may be interested in seeing the coefficient plots or effect plots. Usually, the first step requires predicting the response and then fitting the model (using plot() or ggplot()).

library(ggplot2)

Dotwhisker

The dotwhisker package was designed to easily make dot-and-whisker plots from regression results. We recommend scaling by two standard deviations as recommended by Gelman (2008)..

effects_gt <- broom.mixed::tidy(mod_cw, effect = "fixed", conf.int = TRUE)

dotwhisker::dwplot(effects_gt, by_2sd = TRUE)

Notice that we combined the usage of broom.mixed package to extract the fixed effects (for the dot-and-whisker plots). Details can be found by clicking the url for broom.mixed or see this section.

Emmeans

As its name implies, the emmeans package obtains the estimated marginal means.

More details and examples for lmerMod objects can be found here.

plot(emmeans::emmeans(mod_cw, "Diet"))

SjPlot

The sjPlot package was built for easier plotting and table outputs.

sjPlot::plot_model(mod_cw)

ggEffects

The ggEffects package also computes the marginal effects, returning ready-to-graph results via the ggplot2 package.

The ggEffects documentation already includes more details and examples for lmerMod objects.

predict_ss <- ggeffects::predict_response(mod_ss, "Days [0:9]")
ggplot(predict_ss, aes(x, predicted)) +
  geom_line() + labs(x = "Days", y = "Predicted Reaction") +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.1)

The ggEffects package is supposed to be superseded by modelbased, however, it may run errors when using lmerMod objects.

Marginaleffects

As its package name implies, the goal of marginaleffects is to compute the marginal means as well as various other model comparisons for a lot of different classes, merMod objects being one of them.

More details and examples for mixed models can be found here.

library(marginaleffects)
# Note: datagrid depends on marginaleffects

pred_ss <- predictions(mod_ss, newdata = datagrid(Days = 0:9))
# Plot using ggplot2
ggplot(pred_ss, aes(x = Days, y = estimate)) +
  geom_line() +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.1) +
  labs(x = "Days", y = "Predicted Reaction")

Formatted Regression Tables

Although the output can easily be seen by applying summary() to the merMod object, it can be useful to use packages that automatically format the results nicely into \(\LaTeX\), HTML, and text output. There are many such functions that appear to be compatiable with merMod objects.

In this section, we display the code (though it is not executed) along with the resulting HTML output. The corresponding versions for \(\LaTeX\) and plain text are equally straightforward.

SjPlot

The sjPlot package can be used for table outputs in HTML. There is a fantastic example written in their documentation here.

Parameters

The Parameters package, as its name suggests, extracts parameters from a fitted model. Their own documentation also includes examples of mixed models from lme4 here. and exact table formatting can be found here.

Huxtable

The Huxtable package can be used to create \(\LaTeX\) (see huxtable::print_latex()) and HTML (see huxtable::print_html()) output.

h_tab = huxtable::huxreg(mod_ss)
huxtable::print_html(h_tab)
(1)
(Intercept) 251.405 
(6.825)
Days 10.467 
(1.546)
sd__(Intercept) 24.741 
(NA)     
cor__(Intercept).Days 0.066 
(NA)     
sd__Days 5.922 
(NA)     
sd__Observation 25.592 
(NA)     
N 180     
logLik -871.814 
AIC 1755.628 
*** p < 0.001; ** p < 0.01; * p < 0.05.

Stargazer

Stargazer was intended to create well formatted regression tables in \(\LaTeX\), HTML/CSS, and plain text.

stargazer::stargazer(mod_ss, type = "html") # use type = "latex" for LaTeX
Dependent variable:
Reaction
Days 10.467***
(1.546)
Constant 251.405***
(6.825)
Observations 180
Log Likelihood -871.814
Akaike Inf. Crit. 1,755.628
Bayesian Inf. Crit. 1,774.786
Note: p<0.1; p<0.05; p<0.01

Texreg

We could also use the texreg package to create \(\LaTeX\) texreg::texreg() and HTML texreg::htmlreg() tables.

texreg::htmlreg(mod_ss)
Statistical models
  Model 1
(Intercept) 251.41***
  (6.82)
Days 10.47***
  (1.55)
AIC 1755.63
BIC 1774.79
Log Likelihood -871.81
Num. obs. 180
Num. groups: Subject 18
Var: Subject (Intercept) 612.10
Var: Subject Days 35.07
Cov: Subject (Intercept) Days 9.60
Var: Residual 654.94
***p < 0.001; **p < 0.01; *p < 0.05

Memisc

The goal of the memisc package is to make it easier to deal with survey data sets, such as table formatting in \(\LaTeX\) (using memisc::mtable_format_latex()) and HTML (using memisc::mtable_format_html()).

mem_tab <- memisc::mtable("Model 1"=mod_cw,"Model 2"=mod_cw2,
                          "Model 3"=mod_cw3, summary.stats=c("sigma","R-squared","F","p","N"))

memisc::mtable_format_html(mem_tab)
Model 1 Model 2 Model 3
(Intercept) 11 . 244 10 . 924** 27 . 845***
(5 . 789) (3 . 361) (4 . 388)
Time 8 . 717*** 8 . 750*** 8 . 726***
(0 . 175) (0 . 222) (0 . 176)
Diet: 2/1 16 . 210 16 . 166***
(9 . 464) (4 . 086)
Diet: 3/1 36 . 543*** 36 . 499***
(9 . 464) (4 . 086)
Diet: 4/1 30 . 013** 30 . 233***
(9 . 471) (4 . 107)
N 578 578 578
R-squared 0 . 745
sigma 35 . 993
F 419 . 177
p 0 . 000

Significance: *** = p < 0.001; ** = p < 0.01; * = p < 0.05

Other

This section covers other external packages that may be useful but does not fall under the category of previous sections.

Broom.mixed

The broom package takes output from built-in functions in R and converts them into a tibble, which is an alternative to R’s built in data.frame.

Broom.mixed is a spin-off of broom where it takes outputs from outputs from specifically mixed models from various popular mixed model packages, and of course lme4 is one of them.

broom.mixed::tidy(mod_ss)
## # A tibble: 6 Ă— 6
##   effect   group    term                  estimate std.error statistic
##   <chr>    <chr>    <chr>                    <dbl>     <dbl>     <dbl>
## 1 fixed    <NA>     (Intercept)           251.          6.82     36.8 
## 2 fixed    <NA>     Days                   10.5         1.55      6.77
## 3 ran_pars Subject  sd__(Intercept)        24.7        NA        NA   
## 4 ran_pars Subject  cor__(Intercept).Days   0.0656     NA        NA   
## 5 ran_pars Subject  sd__Days                5.92       NA        NA   
## 6 ran_pars Residual sd__Observation        25.6        NA        NA

Equatiomatic

The equatiomatic package takes a fitted model and writes the equation for you, formatted in \(\LaTeX\).

equatiomatic::extract_eq(mod_ss)

The output provides \(\LaTeX\) code that outputs the following equation: \[ \begin{aligned} \operatorname{Reaction}_{i} &\sim N \left(\alpha_{j[i]} + \beta_{1j[i]}(\operatorname{Days}), \sigma^2 \right) \\ \left( \begin{array}{c} \begin{aligned} &\alpha_{j} \\ &\beta_{1j} \end{aligned} \end{array} \right) &\sim N \left( \left( \begin{array}{c} \begin{aligned} &\mu_{\alpha_{j}} \\ &\mu_{\beta_{1j}} \end{aligned} \end{array} \right) , \left( \begin{array}{cc} \sigma^2_{\alpha_{j}} & \rho_{\alpha_{j}\beta_{1j}} \\ \rho_{\beta_{1j}\alpha_{j}} & \sigma^2_{\beta_{1j}} \end{array} \right) \right) \text{, for Subject j = 1,} \dots \text{,J} \end{aligned} \]

lme4/vignettes/lmerperf.Rmd0000644000176200001440000001321515022107260015400 0ustar liggesusers--- title: "lme4 performance tips" --- ```{r opts, echo = FALSE, message = FALSE} library("knitr") knitr::opts_chunk$set( ) (load(system.file("testdata", "lmerperf.rda", package="lme4")))# 'ss' 'fitlist' ``` ```{r loadpkg,message=FALSE} library("lme4") ``` ## overview In general `lme4`'s algorithms scale reasonably well with the number of observations and the number of random effect levels. The biggest bottleneck is in the number of *top-level parameters*, i.e. covariance parameters for `lmer` fits or `glmer` fits with `nAGQ`=0 [`length(getME(model, "theta"))`], covariance and fixed-effect parameters for `glmer` fits with `nAGQ`>0. `lme4` does a derivative-free (by default) nonlinear optimization step over the top-level parameters. For this reason, "maximal" models involving interactions of factors with several levels each (e.g. `(stimulus*primer | subject)`) will be slow (as well as hard to estimate): if the two factors have `f1` and `f2` levels respectively, then the corresponding `lmer` fit will need to estimate `(f1*f2)*(f1*f2+1)/2` top-level parameters. `lme4` automatically constructs the random effects model matrix ($Z$) as a sparse matrix. At present it does *not* allow an option for a sparse fixed-effects model matrix ($X$), which is useful if the fixed-effect model includes factors with many levels. Treating such factors as random effects instead, and using the modular framework (`?modular`) to fix the variance of this random effect at a large value, will allow it to be modeled using a sparse matrix. (The estimates will converge to the fixed-effect case in the limit as the variance goes to infinity.) ## setting `calc.derivs = FALSE` After finding the best-fit model parameters (in most cases using *derivative-free* algorithms such as Powell's BOBYQA or Nelder-Mead, `[g]lmer` does a series of finite-difference calculations to estimate the gradient and Hessian at the MLE. These are used to try to establish whether the model has converged reliably, and (for `glmer`) to estimate the standard deviations of the fixed effect parameters (a less accurate approximation is used if the Hessian estimate is not available. As currently implemented, this computation takes `2*n^2 - n + 1` additional evaluations of the deviance, where `n` is the total number of top-level parameters. Using `control = [g]lmerControl(calc.derivs = FALSE)` to turn off this calculation can speed up the fit, e.g. ```{r noderivs, eval = FALSE} m0 <- lmer(y ~ service * dept + (1|s) + (1|d), InstEval, control = lmerControl(calc.derivs = FALSE)) ``` ```{r calcs, echo = FALSE} ## based on loaded lmerperf file t1 <- fitlist$basic$times[["elapsed"]] t2 <- fitlist$noderivs$times[["elapsed"]] pct <- round(100*(t1-t2)/t1) e1 <- fitlist$basic$optinfo$feval ``` Benchmark results for this run with and without derivatives show an approximately `r pct`% speedup (from `r round(t1)` to `r round(t2)` seconds on a Linux machine with AMD Ryzen 9 2.2 GHz processors). This is a case with only 2 top-level parameters, but the fit took only `r e1` deviance function evaluations (see `m0@optinfo$feval`) to converge, so the effect of the additional 7 ($n^2 -n +1$) function evaluations is noticeable. ## choice of optimizer ```{r glmeropt, echo=FALSE} gg <- glmerControl()$optimizer ``` `lmer` uses the "`r lmerControl()$optimizer`" optimizer by default; `glmer` uses a combination of `r gg[1]` (`nAGQ=0` stage) and `r gg[2]`. These are reasonably good choices, although switching `glmer` fits to `nloptwrap` for both stages may be worth a try. `allFits()` gives an easy way to check the timings of a large range of optimizers: ```{r times, as.is=TRUE, echo=FALSE} tt <- sort(ss$times[,"elapsed"]) tt2 <- data.frame(optimizer = names(tt), elapsed = tt) rownames(tt2) <- NULL knitr::kable(tt2) ``` As expected, bobyqa - both the implementation in the `minqa` package [`[g]lmerControl(optimizer="bobyqa")`] and the one in `nloptwrap` [`optimizer="nloptwrap"` or `optimizer="nloptwrap", optCtrl = list(algorithm = "NLOPT_LN_BOBYQA"`] - are fastest. ## changing optimizer tolerances Occasionally, the default optimizer stopping tolerances are unnecessarily strict. These tolerances are specific to each optimizer, and can be set via the `optCtrl` argument in `[g]lmerControl`. To see the defaults for `nloptwrap`: ```{r default} environment(nloptwrap)$defaultControl ``` ```{r calcs2, echo = FALSE} ## based on loaded lmerperf file t1 <- fitlist$basic$times[["elapsed"]] t2 <- fitlist$noderivs$times[["elapsed"]] t3 <- fitlist$nlopt_sloppy$times[["elapsed"]] pct <- round(100*(t1-t2)/t1) e1 <- fitlist$basic$optinfo$feval ``` In the particular case of the `InstEval` example, this doesn't help much - loosening the tolerances to `ftol_abs=1e-4`, `xtol_abs=1e-4` only saves 2 functional evaluations and a few seconds, while loosening the tolerances still further gives convergence warnings. ## parallelization/BLAS There are not many options for parallelizing `lme4`. Optimized BLAS does not seem to help much. ## other packages - `glmmTMB` may be faster than `lme4` for GLMMs with large numbers of top-level parameters, especially for negative binomial models (i.e. compared to `glmer.nb`) - the `MixedModels.jl` package in Julia may be *much* faster for some problems. You do need to install Julia. - see [this short tutorial](https://github.com/ginettelafit/MixedModelswithRandJulia) or [this example](https://github.com/RePsychLing/MixedModels-lme4-bridge/blob/master/using_jellyme4.ipynb) (Jupyter notebook) - the [JellyMe4](https://github.com/palday/JellyMe4.jl) and [jglmm](https://github.com/mikabr/jglmm) packages provide R interfaces lme4/vignettes/PLSvGLS.Rnw0000644000176200001440000004356215022107260015012 0ustar liggesusers\documentclass[12pt]{article} \usepackage{Sweave,amsmath,amsfonts,bm} \usepackage[authoryear,round]{natbib} \bibliographystyle{plainnat} \DeclareMathOperator \tr {tr} \DefineVerbatimEnvironment{Sinput}{Verbatim} {formatcom={\vspace{-1ex}},fontshape=sl, fontfamily=courier,fontseries=b, fontsize=\footnotesize} \DefineVerbatimEnvironment{Soutput}{Verbatim} {formatcom={\vspace{-1ex}},fontfamily=courier,fontseries=b,% fontsize=\footnotesize} %%\VignetteIndexEntry{PLS vs GLS for LMMs} %%\VignetteDepends{lme4} \title{Penalized least squares versus generalized least squares representations of linear mixed models} \author{Douglas Bates\\Department of Statistics\\% University of Wisconsin -- Madison} \begin{document} \SweaveOpts{engine=R,eps=FALSE,pdf=TRUE,strip.white=true,keep.source=TRUE} \SweaveOpts{include=FALSE} \setkeys{Gin}{width=\textwidth} \newcommand{\code}[1]{\texttt{\small{#1}}} \newcommand{\package}[1]{\textsf{\small{#1}}} \newcommand{\trans}{\ensuremath{^\prime}} <>= options(width=65,digits=5) #library(lme4) @ \maketitle \begin{abstract} The methods in the \code{lme4} package for \code{R} for fitting linear mixed models are based on sparse matrix methods, especially the Cholesky decomposition of sparse positive-semidefinite matrices, in a penalized least squares representation of the conditional model for the response given the random effects. The representation is similar to that in Henderson's mixed-model equations. An alternative representation of the calculations is as a generalized least squares problem. We describe the two representations, show the equivalence of the two representations and explain why we feel that the penalized least squares approach is more versatile and more computationally efficient. \end{abstract} \section{Definition of the model} \label{sec:Definition} We consider linear mixed models in which the random effects are represented by a $q$-dimensional random vector, $\bm{\mathcal{B}}$, and the response is represented by an $n$-dimensional random vector, $\bm{\mathcal{Y}}$. We observe a value, $\bm y$, of the response. The random effects are unobserved. For our purposes, we will assume a ``spherical'' multivariate normal conditional distribution of $\bm{\mathcal{Y}}$, given $\bm{\mathcal{B}}$. That is, we assume the variance-covariance matrix of $\bm{\mathcal{Y}}|\bm{\mathcal{B}}$ is simply $\sigma^2\bm I_n$, where $\bm I_n$ denotes the identity matrix of order $n$. (The term ``spherical'' refers to the fact that contours of the conditional density are concentric spheres.) The conditional mean, $\mathrm{E}[\bm{\mathcal{Y}}|\bm{\mathcal{B}}=\bm b]$, is a linear function of $\bm b$ and the $p$-dimensional fixed-effects parameter, $\bm\beta$, \begin{equation} \label{eq:condmean} \mathrm{E}[\bm{\mathcal{Y}}|\bm{\mathcal{B}}=\bm b]= \bm X\bm\beta+\bm Z\bm b , \end{equation} where $\bm X$ and $\bm Z$ are known model matrices of sizes $n\times p$ and $n\times q$, respectively. Thus \begin{equation} \label{eq:yconditional} \bm{\mathcal{Y}}|\bm{\mathcal{B}}\sim \mathcal{N}\left(\bm X\bm\beta+\bm Z\bm b,\sigma^2\bm I_n\right) . \end{equation} The marginal distribution of the random effects \begin{equation} \label{eq:remargin} \bm{\mathcal{B}}\sim\mathcal{N}\left(\bm 0,\sigma^2\bm\Sigma(\bm\theta)\right) \end{equation} is also multivariate normal, with mean $\bm 0$ and variance-covariance matrix $\sigma^2\bm\Sigma(\bm\theta)$. The scalar, $\sigma^2$, in (\ref{eq:remargin}) is the same as the $\sigma^2$ in (\ref{eq:yconditional}). As described in the next section, the relative variance-covariance matrix, $\bm\Sigma(\bm\theta)$, is a $q\times q$ positive semidefinite matrix depending on a parameter vector, $\bm\theta$. Typically the dimension of $\bm\theta$ is much, much smaller than $q$. \subsection{Variance-covariance of the random effects} \label{sec:revarcov} The relative variance-covariance matrix, $\bm\Sigma(\bm\theta)$, must be symmetric and positive semidefinite (i.e. $\bm x\trans\bm\Sigma\bm x\ge0,\forall\bm x\in\mathbb{R}^q$). Because the estimate of a variance component can be zero, it is important to allow for a semidefinite $\bm\Sigma$. We do not assume that $\bm\Sigma$ is positive definite (i.e. $\bm x\trans\bm\Sigma\bm x>0,\forall\bm x\in\mathbb{R}^q, \bm x\ne\bm 0$) and, hence, we cannot assume that $\bm\Sigma^{-1}$ exists. A positive semidefinite matrix such as $\bm\Sigma$ has a Cholesky decomposition of the so-called ``LDL$\trans$'' form. We use a slight modification of this form, \begin{equation} \label{eq:TSdef} \bm\Sigma(\bm\theta)=\bm T(\bm\theta)\bm S(\bm\theta)\bm S(\bm\theta)\bm T(\bm\theta)\trans , \end{equation} where $\bm T(\bm\theta)$ is a unit lower-triangular $q\times q$ matrix and $\bm S(\bm\theta)$ is a diagonal $q\times q$ matrix with nonnegative diagonal elements that act as scale factors. (They are the relative standard deviations of certain linear combinations of the random effects.) Thus, $\bm T$ is a triangular matrix and $\bm S$ is a scale matrix. Both $\bm T$ and $\bm S$ are highly patterned. \subsection{Orthogonal random effects} \label{sec:orthogonal} Let us define a $q$-dimensional random vector, $\bm{\mathcal{U}}$, of orthogonal random effects with marginal distribution \begin{equation} \label{eq:Udist} \bm{\mathcal{U}}\sim\mathcal{N}\left(\bm 0,\sigma^2\bm I_q\right) \end{equation} and, for a given value of $\bm\theta$, express $\bm{\mathcal{B}}$ as a linear transformation of $\bm{\mathcal{U}}$, \begin{equation} \label{eq:UtoB} \bm{\mathcal{B}}=\bm T(\bm\theta)\bm S(\bm\theta)\bm{\mathcal{U}} . \end{equation} Note that the transformation (\ref{eq:UtoB}) gives the desired distribution of $\bm{\mathcal{B}}$ in that $\mathrm{E}[\bm{\mathcal{B}}]=\bm T\bm S\mathrm{E}[\bm{\mathcal{U}}]=\bm 0$ and \begin{displaymath} \mathrm{Var}(\bm{\mathcal{B}})=\mathrm{E}[\bm{\mathcal{B}}\bm{\mathcal{B}}\trans] =\bm T\bm S\mathrm{E}[\bm{\mathcal{U}}\bm{\mathcal{U}}\trans]\bm S\bm T\trans=\sigma^2\bm T\bm S\bm S\bm T\trans=\bm\Sigma . \end{displaymath} The conditional distribution, $\bm{\mathcal{Y}}|\bm{\mathcal{U}}$, can be derived from $\bm{\mathcal{Y}}|\bm{\mathcal{B}}$ as \begin{equation} \label{eq:YgivenU} \bm{\mathcal{Y}}|\bm{\mathcal{U}}\sim\mathcal{N}\left(\bm X\bm\beta+\bm Z\bm T\bm S\bm u, \sigma^2\bm I\right) \end{equation} We will write the transpose of $\bm Z\bm T\bm S$ as $\bm A$. Because the matrices $\bm T$ and $\bm S$ depend on the parameter $\bm\theta$, $\bm A$ is also a function of $\bm\theta$, \begin{equation} \label{eq:Adef} \bm A\trans(\bm\theta)=\bm Z\bm T(\bm\theta)\bm S(\bm\theta) . \end{equation} In applications, the matrix $\bm Z$ is derived from indicator columns of the levels of one or more factors in the data and is a \emph{sparse} matrix, in the sense that most of its elements are zero. The matrix $\bm A$ is also sparse. In fact, the structure of $\bm T$ and $\bm S$ are such that pattern of nonzeros in $\bm A$ is that same as that in $\bm Z\trans$. \subsection{Sparse matrix methods} \label{sec:sparseMatrix} The reason for defining $\bm A$ as the transpose of a model matrix is because $\bm A$ is stored and manipulated as a sparse matrix. In the compressed column-oriented storage form that we use for sparse matrices, there are advantages to storing $\bm A$ as a matrix of $n$ columns and $q$ rows. In particular, the CHOLMOD sparse matrix library allows us to evaluate the sparse Cholesky factor, $\bm L(\bm\theta)$, a sparse lower triangular matrix that satisfies \begin{equation} \label{eq:SparseChol} \bm L(\bm\theta)\bm L(\bm\theta)\trans= \bm P\left(\bm A(\bm\theta)\bm A(\bm\theta)\trans+\bm I_q\right)\bm P\trans , \end{equation} directly from $\bm A(\bm\theta)$. In (\ref{eq:SparseChol}) the $q\times q$ matrix $\bm P$ is a ``fill-reducing'' permutation matrix determined from the pattern of nonzeros in $\bm Z$. $\bm P$ does not affect the statistical theory (if $\bm{\mathcal{U}}\sim\mathcal{N}(\bm 0,\sigma^2\bm I)$ then $\bm P\trans\bm{\mathcal{U}}$ also has a $\mathcal{N}(\bm 0,\sigma^2\bm I)$ distribution because $\bm P\bm P\trans=\bm P\trans\bm P=\bm I$) but, because it affects the number of nonzeros in $\bm L$, it can have a tremendous impact on the amount storage required for $\bm L$ and the time required to evaluate $\bm L$ from $\bm A$. Indeed, it is precisely because $\bm L(\bm\theta)$ can be evaluated quickly, even for complex models applied the large data sets, that the \code{lmer} function is effective in fitting such models. \section{The penalized least squares approach to linear mixed models} \label{sec:Penalized} Given a value of $\bm\theta$ we form $\bm A(\bm\theta)$ from which we evaluate $\bm L(\bm\theta)$. We can then solve for the $q\times p$ matrix, $\bm R_{\bm{ZX}}$, in the system of equations \begin{equation} \label{eq:RZX} \bm L(\theta)\bm R_{\bm{ZX}}=\bm P\bm A(\bm\theta)\bm X \end{equation} and for the $p\times p$ upper triangular matrix, $\bm R_{\bm X}$, satisfying \begin{equation} \label{eq:RX} \bm R_{\bm X}\trans\bm R_{\bm X}= \bm X\trans\bm X-\bm R_{\bm{ZX}}\trans\bm R_{\bm{ZX}} \end{equation} The conditional mode, $\tilde{\bm u}(\bm\theta)$, of the orthogonal random effects and the conditional mle, $\widehat{\bm\beta}(\bm\theta)$, of the fixed-effects parameters can be determined simultaneously as the solutions to a penalized least squares problem, \begin{equation} \label{eq:PLS} \begin{bmatrix} \tilde{\bm u}(\bm\theta)\\ \widehat{\bm\beta}(\bm\theta) \end{bmatrix}= \arg\min_{\bm u,\bm\beta}\left\| \begin{bmatrix}\bm y\\\bm 0\end{bmatrix} - \begin{bmatrix} \bm A\trans\bm P\trans & \bm X\\ \bm I_q & \bm 0 \end{bmatrix} \begin{bmatrix}\bm u\\\bm\beta\end{bmatrix} , \right\|^2 \end{equation} for which the solution satisfies \begin{equation} \label{eq:PLSsol} \begin{bmatrix} \bm P\left(\bm A\bm A\trans+\bm I\right)\bm P\trans & \bm P\bm A\bm X\\ \bm X\trans\bm A\trans\bm P\trans & \bm X\trans\bm X \end{bmatrix} \begin{bmatrix} \tilde{\bm u}(\bm\theta)\\ \widehat{\bm\beta}(\bm\theta) \end{bmatrix}= \begin{bmatrix}\bm P\bm A\bm y\\\bm X\trans\bm y\end{bmatrix} . \end{equation} The Cholesky factor of the system matrix for the PLS problem can be expressed using $\bm L$, $\bm R_{\bm Z\bm X}$ and $\bm R_{\bm X}$, because \begin{equation} \label{eq:PLSChol} \begin{bmatrix} \bm P\left(\bm A\bm A\trans+\bm I\right)\bm P\trans & \bm P\bm A\bm X\\ \bm X\trans\bm A\trans\bm P\trans & \bm X\trans\bm X \end{bmatrix} = \begin{bmatrix} \bm L & \bm 0\\ \bm R_{\bm Z\bm X}\trans & \bm R_{\bm X}\trans \end{bmatrix} \begin{bmatrix} \bm L\trans & \bm R_{\bm Z\bm X}\\ \bm 0 & \bm R_{\bm X} \end{bmatrix} . \end{equation} In the \code{lme4} package the \code{"mer"} class is the representation of a mixed-effects model. Several slots in this class are matrices corresponding directly to the matrices in the preceding equations. The \code{A} slot contains the sparse matrix $\bm A(\bm\theta)$ and the \code{L} slot contains the sparse Cholesky factor, $\bm L(\bm\theta)$. The \code{RZX} and \code{RX} slots contain $\bm R_{\bm Z\bm X}(\bm\theta)$ and $\bm R_{\bm X}(\bm\theta)$, respectively, stored as dense matrices. It is not necessary to solve for $\tilde{\bm u}(\bm\theta)$ and $\widehat{\bm\beta}(\bm\theta)$ to evaluate the \emph{profiled} log-likelihood, which is the log-likelihood evaluated $\bm\theta$ and the conditional estimates of the other parameters, $\widehat{\bm\beta}(\bm\theta)$ and $\widehat{\sigma^2}(\bm\theta)$. All that is needed for evaluation of the profiled log-likelihood is the (penalized) residual sum of squares, $r^2$, from the penalized least squares problem (\ref{eq:PLS}) and the determinant $|\bm A\bm A\trans+\bm I|=|\bm L|^2$. Because $\bm L$ is triangular, its determinant is easily evaluated as the product of its diagonal elements. Furthermore, $|\bm L|^2 > 0$ because it is equal to $|\bm A\bm A\trans + \bm I|$, which is the determinant of a positive definite matrix. Thus $\log(|\bm L|^2)$ is both well-defined and easily calculated from $\bm L$. The profiled deviance (negative twice the profiled log-likelihood), as a function of $\bm\theta$ only ($\bm\beta$ and $\sigma^2$ at their conditional estimates), is \begin{equation} \label{eq:profiledDev} d(\bm\theta|\bm y)=\log(|\bm L|^2)+n\left(1+\log(r^2)+\frac{2\pi}{n}\right) \end{equation} The maximum likelihood estimates, $\widehat{\bm\theta}$, satisfy \begin{equation} \label{eq:thetamle} \widehat{\bm\theta}=\arg\min_{\bm\theta}d(\bm\theta|\bm y) \end{equation} Once the value of $\widehat{\bm\theta}$ has been determined, the mle of $\bm\beta$ is evaluated from (\ref{eq:PLSsol}) and the mle of $\sigma^2$ as $\widehat{\sigma^2}(\bm\theta)=r^2/n$. Note that nothing has been said about the form of the sparse model matrix, $\bm Z$, other than the fact that it is sparse. In contrast to other methods for linear mixed models, these results apply to models where $\bm Z$ is derived from crossed or partially crossed grouping factors, in addition to models with multiple, nested grouping factors. The system (\ref{eq:PLSsol}) is similar to Henderson's ``mixed-model equations'' (reference?). One important difference between (\ref{eq:PLSsol}) and Henderson's formulation is that Henderson represented his system of equations in terms of $\bm\Sigma^{-1}$ and, in important practical examples, $\bm\Sigma^{-1}$ does not exist at the parameter estimates. Also, Henderson assumed that equations like (\ref{eq:PLSsol}) would need to be solved explicitly and, as we have seen, only the decomposition of the system matrix is needed for evaluation of the profiled log-likelihood. The same is true of the profiled the logarithm of the REML criterion, which we define later. \section{The generalized least squares approach to linear mixed models} \label{sec:GLS} Another common approach to linear mixed models is to derive the marginal variance-covariance matrix of $\bm{\mathcal{Y}}$ as a function of $\bm\theta$ and use that to determine the conditional estimates, $\widehat{\bm\beta}(\bm\theta)$, as the solution of a generalized least squares (GLS) problem. In the notation of \S\ref{sec:Definition} the marginal mean of $\bm{\mathcal{Y}}$ is $\mathrm{E}[\bm{\mathcal{Y}}]=\bm X\bm\beta$ and the marginal variance-covariance matrix is \begin{equation} \label{eq:marginalvarcovY} \mathrm{Var}(\bm{\mathcal{Y}})=\sigma^2\left(\bm I_n+\bm Z\bm T\bm S\bm S\bm T\trans\bm Z\trans\right)=\sigma^2\left(\bm I_n+\bm A\trans\bm A\right) =\sigma^2\bm V(\bm\theta) , \end{equation} where $\bm V(\bm\theta)=\bm I_n+\bm A\trans\bm A$. The conditional estimates of $\bm\beta$ are often written as \begin{equation} \label{eq:condbeta} \widehat{\bm\beta}(\bm\theta)=\left(\bm X\trans\bm V^{-1}\bm X\right)^{-1}\bm X\trans\bm V^{-1}\bm y \end{equation} but, of course, this formula is not suitable for computation. The matrix $\bm V(\bm\theta)$ is a symmetric $n\times n$ positive definite matrix and hence has a Cholesky factor. However, this factor is $n\times n$, not $q\times q$, and $n$ is always larger than $q$ --- sometimes orders of magnitude larger. Blithely writing a formula in terms of $\bm V^{-1}$ when $\bm V$ is $n\times n$, and $n$ can be in the millions does not a computational formula make. \subsection{Relating the GLS approach to the Cholesky factor} \label{sec:GLStoL} We can use the fact that \begin{equation} \label{eq:Vinv} \bm V^{-1}(\bm\theta)=\left(\bm I_n+\bm A\trans\bm A\right)^{-1}= \bm I_n-\bm A\trans\left(\bm I_q+\bm A\bm A\trans\right)^{-1}\bm A \end{equation} to relate the GLS problem to the PLS problem. One way to establish (\ref{eq:Vinv}) is simply to show that the product \begin{multline*} (\bm I+\bm A\trans\bm A)\left(\bm I-\bm A\trans\left(\bm I+\bm A\bm A\trans\right)^{-1}\bm A\right)\\ \begin{aligned} =&\bm I+\bm A\trans\bm A-\bm A\trans\left(\bm I+\bm A\bm A\trans\right) \left(\bm I+\bm A\bm A\trans\right)^{-1}\bm A\\ =&\bm I+\bm A\trans\bm A-\bm A\trans\bm A\\ =&\bm I . \end{aligned} \end{multline*} Incorporating the permutation matrix $\bm P$ we have \begin{equation} \label{eq:PLA} \begin{aligned} \bm V^{-1}(\bm\theta)=&\bm I_n-\bm A\trans\bm P\trans\bm P\left(\bm I_q+\bm A\bm A\trans\right)^{-1}\bm P\trans\bm P\bm A\\ =&\bm I_n-\bm A\trans\bm P\trans(\bm L\bm L\trans)^{-1}\bm P\bm A\\ =&\bm I_n-\left(\bm L^{-1}\bm P\bm A\right)\trans\bm L^{-1}\bm P\bm A . \end{aligned} \end{equation} Even in this form we would not want to routinely evaluate $\bm V^{-1}$. However, (\ref{eq:PLA}) does allow us to simplify many common expressions. For example, the variance-covariance of the estimator $\widehat{\bm \beta}$, conditional on $\bm\theta$ and $\sigma$, can be expressed as \begin{equation} \label{eq:varcovbeta} \begin{aligned} \sigma^2\left(\bm X\trans\bm V^{-1}(\bm\theta)\bm X\right)^{-1} =&\sigma^2\left(\bm X\trans\bm X-\left(\bm L^{-1}\bm P\bm A\bm X\right)\trans\left(\bm L^{-1}\bm P\bm A\bm X\right)\right)^{-1}\\ =&\sigma^2\left(\bm X\trans\bm X-\bm R_{\bm Z\bm X}\trans\bm R_{\bm Z\bm X}\right)^{-1}\\ =&\sigma^2\left(\bm R_{\bm X}\trans\bm R_{\bm X}\right)^{-1} . \end{aligned} \end{equation} \section{Trace of the ``hat'' matrix} \label{sec:hatTrace} Another calculation that is of interest to some is the the trace of the ``hat'' matrix, which can be written as \begin{multline} \label{eq:hatTrace} \tr\left(\begin{bmatrix}\bm A\trans&\bm X\end{bmatrix} \left(\begin{bmatrix}\bm A\trans&\bm X\\\bm I&\bm0\end{bmatrix}\trans \begin{bmatrix}\bm A\trans&\bm X\\\bm I&\bm0\end{bmatrix}\right)^{-1} \begin{bmatrix}\bm A\\\bm X\trans\end{bmatrix}\right)\\ = \tr\left(\begin{bmatrix}\bm A\trans&\bm X\end{bmatrix} \left(\begin{bmatrix}\bm L&\bm0\\ \bm R_{\bm{ZX}}\trans&\bm R_{\bm X}\trans\end{bmatrix} \begin{bmatrix}\bm L\trans&\bm R_{\bm{ZX}}\\ \bm0&\bm R_{\bm X}\end{bmatrix}\right)^{-1} \begin{bmatrix}\bm A\\\bm X\trans\end{bmatrix}\right) \end{multline} \end{document} lme4/vignettes/autoscale_ref.bib0000644000176200001440000000241315103764661016426 0ustar liggesusers@article{bolker2013strategies, title={Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS}, author={Bolker, Benjamin M and Gardner, Beth and Maunder, Mark and Berg, Casper W and Brooks, Mollie and Comita, Liza and Crone, Elizabeth and Cubaynes, Sarah and Davies, Trevor and de Valpine, Perry and others}, journal={Methods in Ecology and Evolution}, volume={4}, number={6}, pages={501--512}, year={2013}, publisher={Wiley Online Library} } @article{gelman2008scaling, title={Scaling regression inputs by dividing by two standard deviations}, author={Gelman, Andrew}, journal={Statistics in medicine}, volume={27}, number={15}, pages={2865--2873}, year={2008}, publisher={Wiley Online Library} } @article{schielzethSimple2010, title = {Simple Means to Improve the Interpretability of Regression Coefficients: {{Interpretation}} of Regression Coefficients}, shorttitle = {Simple Means to Improve the Interpretability of Regression Coefficients}, author = {Schielzeth, Holger}, year = {2010}, month = feb, journal = {Methods in Ecology and Evolution}, volume = {1}, number = {2}, pages = {103--113}, issn = {2041210X, 2041210X}, doi = {10.1111/j.2041-210X.2010.00012.x}, urldate = {2016-06-08} } lme4/vignettes/autoscale.Rmd0000644000176200001440000001325615103764661015567 0ustar liggesusers--- title: "Autoscaling in lme4" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Autoscaling in lme4} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} bibliography: autoscale_ref.bib --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` Scaling predictors in a linear model (or extended linear models, such as LMMs or GLMMs) so that all of the predictors are on a similar scale (and thus, in general, the estimated parameters should have a similar scale) can improve the behaviour of various computational methods (see e.g. the "Remove eccentricity by scaling" section of @bolker2013strategies). `lmer()` and `glmer()` issue warnings suggesting that users consider re-scaling predictors when the predictor variables are on very different scales. Re-scaling predictors can improve both computation and interpretation [@schielzethSimple2010] of (extended) linear models. However, in cases where researchers want to scale predictors for computational stability, but get parameter estimates on the scale of the original (unscaled) predictors, back-transforming the predictors, covariance matrix, etc., is tedious. `lme4` now allows this scaling to be done automatically, behind the scenes. An example of a warning message due to disparate predictor scales: ```{r setup} library(lme4) set.seed(1) sleepstudy$var1 = runif(nrow(sleepstudy), 1e6, 1e7) fit1 <- lmer(Reaction ~ var1 + Days + (Days | Subject), sleepstudy) ``` Instead of re-fitting with scaled predictors we can use a new (as of `lme4` version >= 1.1.37) argument for `(g)lmerControl`, called `autoscale`, where `scale()` is automatically applied to the continuous covariates, but when delivered to the user, the coefficient estimates and the covariances are back-transformed. This maintains the original interpretation and minimizes user intervention. Hence: ```{r fit} fit2 <- lmer(Reaction ~ var1 + Days + (Days | Subject), control = lmerControl(autoscale = TRUE), sleepstudy) all.equal(fixef(fit1), fixef(fit2)) all.equal(vcov(fit2), vcov(fit2)) ``` ```{r echo = FALSE} ## https://stackoverflow.com/a/67456510/190277 get_val <- function(x) as.numeric(gsub("^.*?(\\d+\\.?\\d+e?[+-]?\\d+).*$", "\\1", x)) get_tol <- function(x,y, dig=1) signif(get_val(all.equal(x, y, tolerance = 0)), dig) ftol <- get_tol(fixef(fit1), fixef(fit2)) vtol <- get_tol(vcov(fit1), vcov(fit2)) ``` The parameters are in fact slightly different (relative difference in fixed effects of $`r vtol`$, and in covariance matrices of $`r ftol`$); these differences will be larger for less numerically stable models (i.e. more complicated or poorly data-constrained). ## Autoscale Mechanism The base function `scale()` is applied to the model matrix `X` as found in `lFormula()` or `glFormula()` from `R/Modular.R`. To back transform, we use the `scaled:center` and `scaled:scale` attributes to reverse the changes found in `fixef.merMod()`, `model.matrix.merMod()`, and `vcov.merMod()` such that the `summary()` output shows the coefficients according to the original scale. Reverting back the changes for `fixef.merMod()`, the $\beta$ coefficients, is not too difficult. However, reverting said changes for the variance-covariance matrix is a bit more involved. The exact modification can be found from the function `scale_vcov()` (found in `R/utilities.R`), and the derivation is explained below. Consider the following estimation of a simple linear model: \[ \widehat{Y} = \widehat{\beta}_{0} + \widehat{\beta}_{1} X^{*}, \] where $X^{*}$ represents the scaled version of $X$. That is, if we let $C$ represent a vector that contains the values for centering and $S$ represent a vector that contains the values for scaling, we have: \[ \widehat{Y} = \widehat{\beta}_{0} + \widehat{\beta}_{1} \left( \frac{X - C}{S} \right) \] \[ \widehat{Y} = \widehat{\beta}_{0} - \sum_{i=1}^{p} \frac{\widehat{\beta}_{i}c_{i}}{s_{i}} + \sum_{i=1}^{p} \frac{\widehat{\beta}_{i} x_{i}}{s_{i}}. \] From the above, it is clear that the new intercept can be represented as: \[ \widehat{\beta}_{0}' = \widehat{\beta}_{0} - \sum_{i=1}^{p} \frac{\widehat{\beta}_{i}c_{i}}{s_{i}}. \] Similarly, the new coefficients are represented as: \[ \widehat{\beta}_{i}' = \frac{\widehat{\beta}_{i}}{s_{i}}. \] Then, the new variance-covariance matrix can be derived using the following: \newcommand{\cov}{\textrm{Cov}}} \[ \cov\left( \frac{\widehat{\beta}_{i}}{s_{i}}, \frac{\widehat{\beta}_{j}}{s_{j}} \right) = \frac{1}{s_{i}s_{j}} \cov(\widehat{\beta}_{i}, \widehat{\beta_{j}}) = \frac{\sigma_{ij}^{2}}{s_{i}s_{j}} \] \begin{equation} \begin{split} \cov\left( \widehat{\beta}_{0} - \sum_{i=1}^{p} \frac{\widehat{\beta}_{i}c_{i}}{s_{i}}, \widehat{\beta}_{0} - \sum_{j=1}^{p} \frac{\widehat{\beta}_{i}c_{i}}{s_{i}} \right) &= \cov(\widehat{\beta}_{0}, \widehat{\beta}_{0} ) - 2 \sum_{i=1}^{p} \frac{c_{i}}{s_{i}} \cov(\widehat{\beta}_{0} , \widehat{\beta}_{i}) + \sum_{i=1}^{p} \sum_{j=1}^{p} \frac{c_{i}c_{j}}{s_{i}s_{j}} \cov(\widehat{\beta}_{i}, \widehat{\beta}_{j}) \\ &= \sigma_{0}^{2} - 2 \sum_{i=1}^{p} \frac{c_{i}}{s_{i}} \sigma_{0i}^{2} + \sum_{i=1}^{p} \sum_{j=1}^{p} \frac{c_{i}c_{j}}{s_{i}s_{j}} \sigma_{ij}^{2} \end{split} \end{equation} \begin{equation} \begin{split} \cov\left( \widehat{\beta}_{0} - \sum_{i=1}^{p} \frac{\widehat{\beta}_{i}c_{i}}{s_{i}}, \frac{\widehat{\beta_{j}}}{s_{j}} \right) &= \cov\left( \widehat{\beta}_{0}, \frac{\widehat{\beta}_{j}}{s_{j}} \right) - \sum_{i=1}^{p} \frac{c_{i}}{s_{i}s_{j}} \cov \left( \widehat{\beta}_{i}, \widehat{\beta}_{j} \right) \\ &= \frac{\sigma_{0j}^{2}}{s_{j}} - \sum_{i=1}^{p} \frac{c_{i}}{s_{i}s_{j}} \sigma_{ij}^{2}, \end{split} \end{equation} for $j = 1, 2, ..., p$. ## References lme4/vignettes/lmer.Rnw0000644000176200001440000041433615103753306014570 0ustar liggesusers%\VignetteEngine{knitr::knitr} %\VignetteDepends{ggplot2} %\VignetteDepends{gamm4} %\VignetteIndexEntry{Fitting Linear Mixed-Effects Models using lme4} \documentclass[nojss]{jss} \usepackage[T1]{fontenc}% for correct hyphenation and T1 encoding \usepackage[utf8]{inputenc}% \usepackage{lmodern}% latin modern font \usepackage[american]{babel} %% for texi2dvi ~ bug \usepackage{bm,amsmath,thumbpdf,amsfonts}%,minted} \usepackage{blkarray} \usepackage{array} %% huxtable-ish stuff %% \usepackage{adjustbox} %% \usepackage{threeparttable} %% \newcolumntype{P}[1]{>{\raggedright\arraybackslash}p{#1}} \newcommand{\matindex}[1]{\mbox{\scriptsize#1}}% Matrix index \newcommand{\github}{Github} \DeclareMathOperator{\tr}{tr} \DeclareMathOperator{\VEC}{vec} \newcommand{\bmb}[1]{{\color{red} \emph{#1}}} \newcommand{\scw}[1]{{\color{blue} \emph{#1}}} \newcommand{\dmb}[1]{{\color{magenta} \emph{#1}}} \shortcites{bolker_strategies_2013,sleepstudy,gelman2013bayesian} \author{Douglas Bates\\University of Wisconsin-Madison\And Martin M\"achler\\ETH Zurich\And Benjamin M. Bolker\\McMaster University\And Steven C. Walker\\McMaster University } \Plainauthor{Douglas Bates, Martin M\"achler, Ben Bolker, Steve Walker} \title{Fitting Linear Mixed-Effects Models Using \pkg{lme4}} \Plaintitle{Fitting Linear Mixed-Effects Models using lme4} \Shorttitle{Linear Mixed Models with lme4} \Abstract{% Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the \code{lmer} function in the \pkg{lme4} package for \proglang{R}. As for most model-fitting functions in \proglang{R}, the model is described in an \code{lmer} call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in \proglang{R}, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by \code{lmer}.} \Keywords{% sparse matrix methods, linear mixed models, penalized least squares, Cholesky decomposition} \Address{ Douglas Bates\\ Department of Statistics, University of Wisconsin\\ 1300 University Ave.\\ Madison, WI 53706, U.S.A.\\ E-mail: \email{bates@stat.wisc.edu}\\ \par\bigskip Martin M\"achler\\ Seminar f\"ur Statistik, HG G~16\\ ETH Zurich\\ 8092 Zurich, Switzerland\\ E-mail: \email{maechler@stat.math.ethz.ch}\\ % URL: \url{http://stat.ethz.ch/people/maechler}\\ \par\bigskip Benjamin M. Bolker\\ Departments of Mathematics \& Statistics and Biology \\ McMaster University \\ 1280 Main Street W \\ Hamilton, ON L8S 4K1, Canada \\ E-mail: \email{bolker@mcmaster.ca}\\ \par\bigskip Steven C. Walker\\ Department of Mathematics \& Statistics \\ McMaster University \\ 1280 Main Street W \\ Hamilton, ON L8S 4K1, Canada \\ E-mail: \email{scwalker@math.mcmaster.ca } } \newcommand{\thetavec}{{\bm\theta}} \newcommand{\betavec}{{\bm\beta}} \newcommand{\Var}{\operatorname{Var}} \newcommand{\abs}{\operatorname{abs}} \newcommand{\bLt}{\ensuremath{\bm\Lambda_{\bm\theta}}} \newcommand{\mc}[1]{\ensuremath{\mathcal{#1}}} \newcommand{\trans}{\ensuremath{^\top}} % JSS wants \top \newcommand{\yobs}{\ensuremath{\bm y_{\mathrm{obs}}}} \newcommand*{\eq}[1]{eqn.~\ref{#1}}% or just {(\ref{#1})} <>= options(width=70, show.signif.stars=FALSE, str=strOptions(strict.width="cut"), ## prefer empty continuation for reader's cut'n'paste: continue = " ", #JSS: prompt = "R> ", continue = "+ ", useFancyQuotes = FALSE) library("knitr") library("lme4") library("ggplot2")# Keeping default theme, nicer "on line": #JSS theme_set(theme_bw()) library("grid") zmargin <- theme(panel.spacing=unit(0,"lines")) library("lattice") library("minqa") library("reformulas") opts_chunk$set(engine='R',dev='pdf', fig.width=9, fig.height=5.5, prompt=TRUE, cache=TRUE, tidy=FALSE, comment=NA, error = FALSE) knitr::render_sweave() @ \setkeys{Gin}{width=\textwidth} \setkeys{Gin}{height=3.5in} \begin{document} A version of this manuscript has been published online in the \emph{Journal of Statistical Software}, on Oct.\ 2015, with DOI \linebreak[3] \texttt{10.18637/jss.v067.i01}, see \url{https://www.jstatsoft.org/article/view/v067i01/}. \section{Introduction} \label{sec:intro} The \pkg{lme4} package \citep{lme4} for \proglang{R} \citep{R} provides functions to fit and analyze linear mixed models, generalized linear mixed models and nonlinear mixed models. In each of these names, the term ``mixed'' or, more fully, ``mixed effects'', denotes a model that incorporates both fixed- and random-effects terms in a linear predictor expression from which the conditional mean of the response can be evaluated. In this paper we describe the formulation and representation of linear mixed models. The techniques used for generalized linear and nonlinear mixed models will be described separately, in a future paper. At present, the main alternative to \pkg{lme4} for mixed modeling in \proglang{R} is the \pkg{nlme} package \citep{nlme_pkg}. The main features distinguishing \pkg{lme4} from \pkg{nlme} are (1) more efficient linear algebra tools, giving improved performance on large problems; (2) simpler syntax and more efficient implementation for fitting models with crossed random effects; (3) the implementation of profile likelihood confidence intervals on random-effects parameters; and (4) the ability to fit generalized linear mixed models (although in this paper we restrict ourselves to linear mixed models). The main advantage of \pkg{nlme} relative to \pkg{lme4} is a user interface for fitting models with structure in the residuals (various forms of heteroscedasticity and autocorrelation) and in the random-effects covariance matrices (e.g., compound symmetric models). With some extra effort, the computational machinery of \pkg{lme4} can be used to fit structured models that the basic \code{lmer} function cannot handle (see Appendix~\ref{sec:modularExamples}). The development of general software for fitting mixed models remains an active area of research with many open problems. Consequently, the \pkg{lme4} package has evolved since it was first released, and continues to improve as we learn more about mixed models. However, we recognize the need to maintain stability and backward compatibility of \pkg{lme4} so that it continues to be broadly useful. In order to maintain stability while continuing to advance mixed-model computation, we have developed several additional frameworks that draw on the basic ideas of \pkg{lme4} but modify its structure or implementation in various ways. These descendants include the \mbox{\pkg{MixedModels}} package \citep{MixedModels} in \proglang{Julia} \citep{Julia}, the \pkg{lme4pureR} package \citep{lme4pureR} in \proglang{R}, and the \pkg{flexLambda} development branch of \pkg{lme4}. The current article is largely restricted to describing the current stable version of the \pkg{lme4} package (1.1-7), with Appendix~\ref{sec:modularExamples} describing hooks into the computational machinery that are designed for extension development. The \pkg{gamm4} \citep{gamm4} and \pkg{blme} \citep{blme, blme2} packages currently make use of these hooks. Another goal of this article is to contrast the approach used by \pkg{lme4} with previous formulations of mixed models. The expressions for the profiled log-likelihood and profiled REML (restricted maximum likelihood) criteria derived in Section~\ref{sec:profdev} are similar to those presented in \citet{bates04:_linear} and, indeed, are closely related to ``Henderson's mixed-model equations''~\citep{henderson_1982}. Nonetheless there are subtle but important changes in the formulation of the model and in the structure of the resulting penalized least squares (PLS) problem to be solved (Section~\ref{sec:PLSpureR}). We derive the current version of the PLS problem (Section~\ref{sec:plsMath}) and contrast this result with earlier formulations (Section~\ref{sec:previous_lmm_form}). This article is organized into four main sections (Sections~\ref{sec:lFormula}, \ref{sec:mkLmerDevfun}, \ref{sec:optimizeLmer}, and \ref{sec:mkMerMod}), each of which corresponds to one of the four largely separate modules that comprise \pkg{lme4}. Before describing the details of each module, we describe the general form of the linear mixed model underlying \pkg{lme4} (Section~\ref{sec:LMMs}); introduce the \code{sleepstudy} data that will be used as an example throughout (Section~\ref{sec:sleepstudy}); and broadly outline \pkg{lme4}'s modular structure (Section~\ref{sec:modular}). \subsection{Linear mixed models} \label{sec:LMMs} Just as a linear model is described by the distribution of a vector-valued random response variable, $\mc{Y}$, whose observed value is $\yobs$, a linear mixed model is described by the distribution of two vector-valued random variables: $\mc{Y}$, the response, and $\mc{B}$, the vector of random effects. In a linear model the distribution of $\mc Y$ is multivariate normal,%\begin{linenomath} \begin{equation} \label{eq:linearmodel} \mc Y\sim\mc{N}(\bm X\bm\beta+\bm o,\sigma^2\bm W^{-1}), \end{equation} where $n$ is the dimension of the response vector, $\bm W$ is a diagonal matrix of known prior weights, $\bm\beta$ is a $p$-dimensional coefficient vector, $\bm X$ is an $n\times p$ model matrix, and $\bm o$ is a vector of known prior offset terms. The parameters of the model are the coefficients $\bm\beta$ and the scale parameter $\sigma$. In a linear mixed model it is the \emph{conditional} distribution of $\mc Y$ given $\mc B=\bm b$ that has such a form, \begin{equation} \label{eq:LMMcondY} ( \mc Y|\mc B=\bm b)\sim\mc{N}(\bm X\bm\beta+\bm Z\bm b+\bm o,\sigma^2\bm W^{-1}), % | <- for ESS \end{equation} where $\bm Z$ is the $n\times q$ model matrix for the $q$-dimensional vector-valued random-effects variable, $\mc B$, whose value we are fixing at $\bm b$. The unconditional distribution of $\mc B$ is also multivariate normal with mean zero and a parameterized $q\times q$ variance-covariance matrix, $\bm\Sigma$, \begin{equation} \label{eq:LMMuncondB} \mc B\sim\mc N(\bm0,\bm\Sigma) . \end{equation} As a variance-covariance matrix, $\bm\Sigma$ must be positive semidefinite. It is convenient to express the model in terms of a \emph{relative covariance factor}, $\bLt$, which is a $q\times q$ matrix, depending on the \emph{variance-component parameter}, $\bm\theta$, and generating the symmetric $q\times q$ variance-covariance matrix, $\bm\Sigma$, according to%\begin{linenomath} \begin{equation} \label{eq:relcovfac} \bm\Sigma_{\bm\theta}=\sigma^2\bLt\bLt\trans , \end{equation}%\end{linenomath} where $\sigma$ is the same scale factor as in the conditional distribution (\ref{eq:LMMcondY}). Although Equations~\ref{eq:LMMcondY}, \ref{eq:LMMuncondB}, and \ref{eq:relcovfac} fully describe the class of linear mixed models that \pkg{lme4} can fit, this terse description hides many important details. Before moving on to these details, we make a few observations: \begin{itemize} \item This formulation of linear mixed models allows for a relatively compact expression for the profiled log-likelihood of $\bm\theta$ (Section~\ref{sec:profdev}, Equation~\ref{eq:profiledDeviance}). \item The matrices associated with random effects, $\bm Z$ and $\bLt$, typically have a sparse structure with a sparsity pattern that encodes various model assumptions. Sections~\ref{sec:LMMmatrix} and \ref{sec:CSCmats} provide details on these structures, and how to represent them efficiently. \item The interface provided by \pkg{lme4}'s \code{lmer} function is slightly less general than the model described by Equations~\ref{eq:LMMcondY}, \ref{eq:LMMuncondB}, and \ref{eq:relcovfac}. To take advantage of the entire range of possibilities, one may use the modular functions (Sections~\ref{sec:modular} and Appendix~\ref{sec:modularExamples}) or explore the experimental \pkg{flexLambda} branch of \pkg{lme4} on \github. \end{itemize} \subsection{Example} \label{sec:sleepstudy} Throughout our discussion of \pkg{lme4}, we will work with a data set on the average reaction time per day for subjects in a sleep deprivation study \citep{sleepstudy}. On day 0 the subjects had their normal amount of sleep. Starting that night they were restricted to 3 hours of sleep per night. The response variable, \code{Reaction}, represents average reaction times in milliseconds (ms) on a series of tests given each \code{Day} to each \code{Subject} (Figure~\ref{fig:sleepPlot}), % <>= str(sleepstudy) @ <>= ## BMB: seemed more pleasing to arrange by increasing slope rather than ## intercept ... xyplot(Reaction ~ Days | Subject, sleepstudy, aspect = "xy", layout = c(9, 2), type = c("g", "p", "r"), index.cond = function(x, y) coef(lm(y ~ x))[2], xlab = "Days of sleep deprivation", ylab = "Average reaction time (ms)", as.table = TRUE) @ % | Each subject's reaction time increases approximately linearly with the number of sleep-deprived days. However, subjects also appear to vary in the slopes and intercepts of these relationships, which suggests a model with random slopes and intercepts. As we shall see, such a model may be fitted by minimizing the REML criterion (Equation~\ref{eq:REMLdeviance}) using <>= fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) @ % | The estimates of the standard deviations of the random effects for the intercept and the slope are \Sexpr{round(sqrt(VarCorr(fm1)$Subject[1,1]), 2)} ms % $ and \Sexpr{round(sqrt(VarCorr(fm1)$Subject[2,2]), 2)} ms/day. % $ The fixed-effects coefficients, $\betavec$, are \Sexpr{round(fixef(fm1)[1], 1)} ms and \Sexpr{round(fixef(fm1)[2], 2)} ms/day for the intercept and slope. In this model, one interpretation of these fixed effects is that they are the estimated population mean values of the random intercept and slope (Section~\ref{sec:intuitiveFormulas}). We have chosen the \code{sleepstudy} example because it is a relatively small and simple example to illustrate the theory and practice underlying \code{lmer}. However, \code{lmer} is capable of fitting more complex mixed models to larger data sets. For example, we direct the interested reader to \code{RShowDoc("lmerperf", package = "lme4")} for examples that more thoroughly exercise the performance capabilities of \code{lmer}. \subsection{High-level modular structure} \label{sec:modular} The \code{lmer} function is composed of four largely independent modules. In the first module, a mixed-model formula is parsed and converted into the inputs required to specify a linear mixed model (Section~\ref{sec:lFormula}). The second module uses these inputs to construct an \proglang{R} function which takes the covariance parameters, $\bm\theta$, as arguments and returns negative twice the log profiled likelihood or the REML criterion (Section~\ref{sec:mkLmerDevfun}). The third module optimizes this objective function to produce maximum likelihood (ML) or REML estimates of $\bm\theta$ (Section~\ref{sec:optimizeLmer}). Finally, the fourth module provides utilities for interpreting the optimized model (Section~\ref{sec:mkMerMod}). \begin{table}[tb] \centering \begin{tabular}{lllp{2.1in}} \hline Module & & \proglang{R} function & Description \\ \hline Formula module & (Section~\ref{sec:lFormula}) & \code{lFormula} & Accepts a mixed-model formula, data, and other user inputs, and returns a list of objects required to fit a linear mixed model. \\ Objective function module & (Section~\ref{sec:mkLmerDevfun}) & \code{mkLmerDevfun} & Accepts the results of \code{lFormula} and returns a function to calculate the deviance (or restricted deviance) as a function of the covariance parameters, $\bm\theta$.\\ Optimization module & (Section~\ref{sec:optimizeLmer}) & \code{optimizeLmer} & Accepts a deviance function returned by \code{mkLmerDevfun} and returns the results of the optimization of that deviance function. \\ Output module & (Section~\ref{sec:mkMerMod}) & \code{mkMerMod} & Accepts an optimized deviance function and packages the results into a useful object. \\ \hline \end{tabular} \caption{The high-level modular structure of \code{lmer}.} \label{tab:modular} \end{table} To illustrate this modularity, we recreate the \code{fm1} object by a series of four modular steps; the formula module, <>= parsedFormula <- lFormula(formula = Reaction ~ Days + (Days | Subject), data = sleepstudy) @ the objective function module, <>= devianceFunction <- do.call(mkLmerDevfun, parsedFormula) @ the optimization module, <>= optimizerOutput <- optimizeLmer(devianceFunction) @ and the output module, <>= mkMerMod( rho = environment(devianceFunction), opt = optimizerOutput, reTrms = parsedFormula$reTrms, fr = parsedFormula$fr) @ % | \section{Formula module} \label{sec:lFormula} \subsection{Mixed-model formulas} \label{sec:formulas} Like most model-fitting functions in \proglang{R}, \code{lmer} takes as its first two arguments a \emph{formula} specifying the model and the \emph{data} with which to evaluate the formula. This second argument, \code{data}, is optional but recommended and is usually the name of an \proglang{R} data frame. In the \proglang{R} \code{lm} function for fitting linear models, formulas take the form \verb+resp ~ expr+, where \code{resp} determines the response variable and \code{expr} is an expression that specifies the columns of the model matrix. Formulas for the \code{lmer} function contain special random-effects terms, <>= resp ~ FEexpr + (REexpr1 | factor1) + (REexpr2 | factor2) + ... @ where \code{FEexpr} is an expression determining the columns of the fixed-effects model matrix, $\bm X$, and the random-effects terms, \code{(REexpr1 | factor1)} and \code{(REexpr2 | factor2)}, determine both the random-effects model matrix, $\bm Z$ (Section~\ref{sec:mkZ}), and the structure of the relative covariance factor, $\bLt$ (Section~\ref{sec:mkLambdat}). In principle, a mixed-model formula may contain arbitrarily many random-effects terms, but in practice the number of such terms is typically low. \subsection{Understanding mixed-model formulas} \label{sec:intuitiveFormulas} Before describing the details of how \pkg{lme4} parses mixed-model formulas (Section~\ref{sec:LMMmatrix}), we provide an informal explanation and then some examples. Our discussion assumes familiarity with the standard \proglang{R} modeling paradigm \citep{Chambers:1993}. Each random-effects term is of the form \code{(expr | factor)}. The expression \code{expr} is evaluated as a linear model formula, producing a model matrix following the same rules used in standard \proglang{R} modeling functions (e.g., \code{lm} or \code{glm}). The expression \code{factor} is evaluated as an \proglang{R} factor. One way to think about the vertical bar operator is as a special kind of interaction between the model matrix and the grouping factor. This interaction ensures that the columns of the model matrix have different effects for each level of the grouping factor. What makes this a special kind of interaction is that these effects are modeled as unobserved random variables, rather than unknown fixed parameters. Much has been written about important practical and philosophical differences between these two types of interactions \citep[e.g., ][]{henderson_1982,gelman2005analysis}. For example, the random-effects implementation of such interactions can be used to obtain shrinkage estimates of regression coefficients \citep[e.g., ][]{1977EfronAndMorris}, or account for lack of independence in the residuals due to block structure or repeated measurements \citep[e.g., ][]{laird_ware_1982}. Table~\ref{tab:formulas} provides several examples of the right-hand-sides of mixed-model formulas. The first example, \code{(1 | g)}, % | is the simplest possible mixed-model formula, where each level of the grouping factor, \code{g}, has its own random intercept. The mean and standard deviation of these intercepts are parameters to be estimated. Our description of this model incorporates any nonzero mean of the random effects as fixed-effects parameters. If one wishes to specify that a random intercept has \emph{a priori} known means, one may use the \code{offset} function as in the second model in Table~\ref{tab:formulas}. This model contains no fixed effects, or more accurately the fixed-effects model matrix, $\bm X$, has zero columns and $\bm\beta$ has length zero. \begin{table}[tb] \centering \begin{tabular}{llP{1.5in}} %% see new column type for ragged right \hline Formula & Alternative & Meaning \\ \hline%------------------------------------------------ \code{(1 | g)} & \code{1 + (1 | g)} & Random intercept with fixed mean. \\ \code{0 + offset(o) + (1 | g)} & \code{-1 + offset(o) + (1 | g)} & Random intercept with \emph{a priori} means. \\ \code{(1 | g1/g2)} & \code{(1 | g1)+(1 | g1:g2)} % | & Intercept varying among \code{g1} and \code{g2} within \code{g1}. \\ \code{(1 | g1) + (1 | g2)} & \code{1 + (1 | g1) + (1 | g2)}. & Intercept varying among \code{g1} and \code{g2}. \\ \code{x + (x | g)} & \code{1 + x + (1 + x | g)} & Correlated random intercept and slope. \\ \code{x + (x || g)} & \code{1 + x + (1 | g) + (0 + x | g)} & Uncorrelated random intercept and slope. \\ \hline \end{tabular} \caption{Examples of the right-hand-sides of mixed-effects model formulas. The names of grouping factors are denoted \code{g}, \code{g1}, and \code{g2}, and covariates and \emph{a priori} known offsets as \code{x} and \code{o}.} \label{tab:formulas} \end{table} We may also construct models with multiple grouping factors. For example, if the observations are grouped by \code{g2}, which is nested within \code{g1}, then the third formula in Table \ref{tab:formulas} can be used to model variation in the intercept. A common objective in mixed modeling is to account for such nested (or hierarchical) structure. However, one of the most useful aspects of \pkg{lme4} is that it can be used to fit random effects associated with non-nested grouping factors. For example, suppose the data are grouped by fully crossing two factors, \code{g1} and \code{g2}, then the fourth formula in Table \ref{tab:formulas} may be used. Such models are common in item response theory, where \code{subject} and \code{item} factors are fully crossed \citep{doran2007estimating}. In addition to varying intercepts, we may also have varying slopes (e.g., the \code{sleepstudy} data, Section~\ref{sec:sleepstudy}). The fifth example in Table~\ref{tab:formulas} gives a model where both the intercept and slope vary among the levels of the grouping factor. \subsubsection{Specifying uncorrelated random effects} \label{sec:uncor} By default, \pkg{lme4} assumes that all coefficients associated with the same random-effects term are correlated. To specify an uncorrelated slope and intercept (for example), one may either use double-bar notation, \code{(x || g)}, or equivalently use multiple random-effects terms, \code{x + (1 | g) + (0 + x | g)}, as in the final example of Table~\ref{tab:formulas}. For example, if one examined the results of model \code{fm1} of the \code{sleepstudy} data (Section~\ref{sec:sleepstudy}) using \code{summary(fm1)}, one would see that the estimated correlation between the slope for \code{Days} and the intercept is fairly low (\Sexpr{round(attr(VarCorr(fm1)$Subject, "correlation")[2],3)}) % $ (See Section~\ref{sec:summary} below for more on how to extract the random-effects covariance matrix.) We may use double-bar notation to fit a model that excludes a correlation parameter: <>= fm2 <- lmer(Reaction ~ Days + (Days || Subject), sleepstudy) @ Although mixed models where the random slopes and intercepts are assumed independent are commonly used to reduce the complexity of random-slopes models, they do have one subtle drawback. Models in which the slopes and intercepts are allowed to have a nonzero correlation (e.g., \code{fm1}) are invariant to additive shifts of the continuous predictor (\code{Days} in this case). This invariance breaks down when the correlation is constrained to zero; any shift in the predictor will necessarily lead to a change in the estimated correlation, and in the likelihood and predictions of the model. For example, we can eliminate the correlation in \code{fm1} simply by adding an amount equal to the ratio of the estimated among-subject standard deviations multiplied by the estimated correlation (i.e., $\sigma_{\text{\small slope}}/\sigma_{\text{\small intercept}} \cdot \rho_{\text{\small slope:intercept}}$) to the \code{Days} variable. The use of models such as \code{fm2} should ideally be restricted to cases where the predictor is measured on a ratio scale (i.e., the zero point on the scale is meaningful, not just a location defined by convenience or convention), as is the case here. %% <>= %% sleepstudyShift <- within(sleepstudy, { %% Days <- Days + (24.74*0.07)/5.92 }) %% lmer(Reaction ~ Days + (Days | Subject), sleepstudyShift) %% @ \subsection{Algebraic and computational account of mixed-model formulas} \label{sec:LMMmatrix} The fixed-effects terms of a mixed-model formula are parsed to produce the fixed-effects model matrix, $\bm X$, in the same way that the \proglang{R} \code{lm} function generates model matrices. However, a mixed-model formula incorporates $k\ge1$ random-effects terms of the form \code{(r | f)} as well. % | These $k$ terms are used to produce the random-effects model matrix, $\bm Z$ (Equation~\ref{eq:LMMcondY}; Section~\ref{sec:mkZ}), and the structure of the relative covariance factor, $\bLt$ (Equation~\ref{eq:relcovfac}; Section~\ref{sec:mkLambdat}), which are matrices that typically have a sparse structure. We now describe how one might construct these matrices from the random-effects terms, considering first a single term, \code{(r | f)}, % | and then generalizing to multiple terms. Tables~\ref{tab:dim} and \ref{tab:algebraic} summarize the matrices and vectors that determine the structure of $\bm Z$ and $\bLt$. \begin{table}[tb] \centering \begin{tabular}{lll} \hline Symbol & Size \\ \hline $n$ & Length of the response vector, $\mc{Y}$ \\ $p$ & Number of columns of fixed-effects model matrix, $\bm X$ \\ $q = \sum_i^k q_i$ & Number of columns of random-effects model matrix, $\bm Z$ \\ $p_i$ & Number of columns of the raw model matrix, $\bm X_i$ \\ $\ell_i$ & Number of levels of the grouping factor indices, $\bm i_i$ \\ $q_i = p_i\ell_i$ & Number of columns of the term-wise model matrix, $\bm Z_i$ \\ $k$ & Number of random-effects terms \\ $m_i = \binom{p_i+1}{2}$ & Number of covariance parameters for term $i$ \\ $m = \sum_i^k m_i$ & Total number of covariance parameters \\ \hline \end{tabular} \caption{Dimensions of linear mixed models. The subscript $i = 1, \dots, k$ denotes a specific random-effects term.} \label{tab:dim} \end{table} \begin{table}[tb] \centering \begin{tabular}{lll} \hline Symbol & Size & Description \\ \hline $\bm X_i$ & $n\times p_i$ & Raw random-effects model matrix \\ $\bm J_i$ & $n\times \ell_i$ & Indicator matrix of grouping factor indices\\ $\bm X_{ij}$ & $p_i\times 1$ & Column vector containing $j$th row of $\bm X_i$ \\ $\bm J_{ij}$ & $\ell_i\times 1$ & Column vector containing $j$th row of $\bm J_i$ \\ $\bm i_i$ & $n$ & Vector of grouping factor indices \\ $\bm Z_i$ & $n\times q_i$ & Term-wise random-effects model matrix \\ $\bm\theta$ & $m$ & Covariance parameters \\ $\bm T_i$ & $p_i\times p_i$ & Lower triangular template matrix \\ $\bm\Lambda_i$ & $q_i\times q_i$ & Term-wise relative covariance factor \\ \hline \end{tabular} \caption{Symbols used to describe the structure of the random-effects model matrix and the relative covariance factor. The subscript $i = 1, \dots, k$ denotes a specific random-effects term.} \label{tab:algebraic} \end{table} The expression, \code{r}, is a linear model formula that evaluates to an \proglang{R} model matrix, $\bm X_i$, of size $n\times p_i$, called the \emph{raw random-effects model matrix} for term $i$. A term is said to be a \emph{scalar} random-effects term when $p_i=1$, otherwise it is \emph{vector-valued}. For a \emph{simple, scalar} random-effects term of the form \code{(1 | f)}, $\bm X_i$ is the % | $n\times 1$ matrix of ones, which implies a random intercept model. The expression \code{f} evaluates to an \proglang{R} factor, called the \emph{grouping factor}, for the term. For the $i$th term, we represent this factor mathematically with a vector $\bm i_i$ of \emph{factor indices}, which is an $n$-vector of values from $1,\dots,\ell_i$.\footnote{In practice, fixed-effects model matrices and random-effects terms are evaluated with respect to a \emph{model frame}, ensuring that any expressions for grouping factors have been coerced to factors and any unused levels of these factors have been dropped. That is, $\ell_i$, the number of levels in the grouping factor for the $i$th random-effects term, is well-defined.} Let $\bm J_i$ be the $n\times \ell_i$ matrix of indicator columns for $\bm i_i$. Using the \pkg{Matrix} package \citep{Matrix_pkg} in \proglang{R}, we may construct the transpose of $\bm J_i$ from a factor vector, \code{f}, by coercing \code{f} to a `\code{sparseMatrix}' object. For example, <>= set.seed(2) @ <>= (f <- gl(3, 2)) (Ji <- t(as(f, Class = "sparseMatrix"))) @ When $k>1$ we order the random-effects terms so that $\ell_1\ge\ell_2\ge\dots\ge\ell_k$; in general, this ordering reduces ``fill-in'' (i.e., the proportion of elements that are zero in the lower triangle of $\bLt\trans\bm Z\trans\bm W\bm Z\bLt+\bm I$ but not in the lower triangle of its left Cholesky factor, $\bm L_{\bm\theta}$, described below in Equation~\ref{eq:blockCholeskyDecomp}). This reduction in fill-in provides more efficient matrix operations within the penalized least squares algorithm (Section~\ref{sec:plsMath}). \subsubsection{Constructing the random-effects model matrix} \label{sec:mkZ} The $i$th random-effects term contributes $q_i=\ell_ip_i$ columns to the model matrix $\bm Z$. We group these columns into a matrix, $\bm Z_i$, which we refer to as the \emph{term-wise model matrix} for the $i$th term. Thus $q$, the number of columns in $\bm Z$ and the dimension of the random variable, $\mc{B}$, is \begin{equation} \label{eq:qcalc} q=\sum_{i=1}^k q_i = \sum_{i=1}^k \ell_i\,p_i . \end{equation} Creating the matrix $\bm Z_i$ from $\bm X_i$ and $\bm J_i$ is a straightforward concept that is, nonetheless, somewhat awkward to describe. Consider $\bm Z_i$ as being further decomposed into $\ell_i$ blocks of $p_i$ columns. The rows in the first block are the rows of $\bm X_i$ multiplied by the 0/1 values in the first column of $\bm J_i$ and similarly for the subsequent blocks. With these definitions we may define the term-wise random-effects model matrix, $\bm Z_i$, for the $i$th term as a transposed Khatri-Rao product, \begin{equation} \label{eq:Zi} \bm Z_i = (\bm J_i\trans * \bm X_i\trans)\trans = \begin{bmatrix} \bm J_{i1}\trans \otimes \bm X_{i1}\trans \\ \bm J_{i2}\trans \otimes \bm X_{i2}\trans \\ \vdots \\ \bm J_{in}\trans \otimes \bm X_{in}\trans \\ \end{bmatrix}, \end{equation} where $*$ and $\otimes$ are the Khatri-Rao\footnote{Note that the original definition of the Khatri-Rao product is more general than the definition used in the \pkg{Matrix} package, which is the definition we use here.} \citep{khatri1968solutions} and Kronecker products, and $\bm J_{ij}\trans$ and $\bm X_{ij}\trans$ are row vectors of the $j$th rows of $\bm J_i$ and $\bm X_i$. These rows correspond to the $j$th sample in the response vector, $\mc Y$, and thus $j$ runs from $1, \dots, n$. The \pkg{Matrix} package for \proglang{R} contains a \code{KhatriRao} function, which can be used to form $\bm Z_i$. For example, if we begin with a raw model matrix, <>= (Xi <- cbind(1, rep.int(c(-1, 1), 3L))) @ then the term-wise random-effects model matrix is, <>= (Zi <- t(KhatriRao(t(Ji), t(Xi)))) @ <>= ## alternative formulation of Zi (eq:Zi) rbind( Ji[1,] %x% Xi[1,], Ji[2,] %x% Xi[2,], Ji[3,] %x% Xi[3,], Ji[4,] %x% Xi[4,], Ji[5,] %x% Xi[5,], Ji[6,] %x% Xi[6,]) @ In particular, for a simple, scalar term, $\bm Z_i$ is exactly $\bm J_i$, the matrix of indicator columns. For other scalar terms, $\bm Z_i$ is formed by element-wise multiplication of the single column of $\bm X_i$ by each of the columns of $\bm J_i$. Because each $\bm Z_i$ is generated from indicator columns, its cross-product, $\bm Z_i\trans\bm Z_i$ is block-diagonal consisting of $\ell_i$ diagonal blocks each of size $p_i$.\footnote{To see this, note that by the properties of Kronecker products we may write the cross-product matrix $Z_i\trans Z_i$ as $\sum_{j=1}^n \bm J_{ij} \bm J_{ij}\trans \otimes \bm X_{ij} \bm X_{ij}\trans$. Because $\bm J_{ij}$ is a unit vector along a coordinate axis, the cross-product $\bm J_{ij} \bm J_{ij}\trans$ is a $p_i\times p_i$ matrix of all zeros except for a single $1$ along the diagonal. Therefore, the cross-products, $\bm X_{ij} \bm X_{ij}\trans$, will be added to one of the $\ell_i$ blocks of size $p_i\times p_i$ along the diagonal of $Z_i\trans Z_i$.} Note that this means that when $k=1$ (i.e., there is only one random-effects term, and $\bm Z_i = \bm Z$), $\bm Z\trans\bm Z$ will be block diagonal. These block-diagonal properties allow for more efficient sparse matrix computations (Section~\ref{sec:CSCmats}). The full random-effects model matrix, $\bm Z$, is constructed from $k\ge 1$ blocks, \begin{equation} \label{eq:Z} \bm Z = \begin{bmatrix} \bm Z_1 & \bm Z_2 & \hdots & \bm Z_k \\ \end{bmatrix}. \end{equation} By transposing Equation~\ref{eq:Z} and substituting in Equation~\ref{eq:Zi}, we may represent the structure of the transposed random-effects model matrix as follows, \begin{equation} \label{eq:Zt} \bm Z\trans = \begin{blockarray}{ccccc} \text{sample 1} & \text{sample 2} & \hdots & \text{sample } n & \\ \begin{block}{[cccc]c} \bm J_{11} \otimes \bm X_{11} & \bm J_{12} \otimes \bm X_{12} & \hdots & \bm J_{1n} \otimes \bm X_{1n} & \text{term 1} \\ \bm J_{21} \otimes \bm X_{21} & \bm J_{22} \otimes \bm X_{22} & \hdots & \bm J_{2n} \otimes \bm X_{2n} & \text{term 2} \\ \vdots & \vdots & \ddots & \vdots & \vdots \\ \end{block} \end{blockarray}. \end{equation} Note that the proportion of elements of $Z\trans$ that are structural zeros is \begin{equation} \label{eq:ZtSparsity} \frac{\sum_{i=1}^k p_i(\ell_i - 1)}{\sum_{i=1}^k p_i} \qquad . \end{equation} Therefore, the sparsity of $\bm Z\trans$ increases with the number of grouping factor levels. As the number of levels is often large in practice, it is essential for speed and efficiency to take account of this sparsity, for example by using sparse matrix methods, when fitting mixed models (Section~\ref{sec:CSCmats}). \subsubsection{Constructing the relative covariance factor} \label{sec:mkLambdat} The $q\times q$ covariance factor, $\bLt$, is a block diagonal matrix whose $i$th diagonal block, $\bm\Lambda_i$, is of size $q_i,i=1,\dots,k$. We refer to $\bm\Lambda_i$ as the \emph{term-wise relative covariance factor}. Furthermore, $\bm\Lambda_i$ is a homogeneous block diagonal matrix with each of the $\ell_i$ lower-triangular blocks on the diagonal being a copy of a $p_i\times p_i$ lower-triangular \emph{template matrix}, $\bm T_i$. The covariance parameter vector, $\bm\theta$, of length $m_i =\binom{p_i+1}{2}$, consists of the elements in the lower triangle of $\bm T_i,i=1,\dots,k$. To provide a unique representation we require that the diagonal elements of the $\bm T_i,i=1,\dots,k$ be non-negative. The template, $\bm T_i$, can be constructed from the number $p_i$ alone. In \proglang{R} code we denote $p_i$ as \code{nc}. For example, if we set \code{nc <- 3}\Sexpr{nc <- 3}, we could create the template for term $i$ as, <>= nc <- 3 @ %% sequence() is equivalent to unlist(lapply(nvec, seq_len)) %% and (?sequence) ``mainly exists in reverence to the very early history of R'' %% scw: i like sequence, and in fact i never understood why that %% statement is there in the help file. <