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Type 'q()' to quit R. > if (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 > > proc.time() user system elapsed 1.10 0.10 1.21