if (lme4:::testLevel() > 1 || .Platform$OS.type!="windows") {

    ## 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:

    ##' <description>
    ##'
    ##' <details>
    ##' @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
