Running ‘AAAtest-all.R’ [131s/171s]
  Running ‘HSAURtrees.R’ [2s/4s]
  Running ‘REMLdev.R’ [2s/3s]
  Running ‘ST.R’ [2s/3s]
  Running ‘agridat_gotway.R’ [3s/5s]
  Running ‘bootMer.R’ [6s/16s]
  Running ‘boundary.R’ [8s/10s]
  Running ‘confint.R’ [3s/4s]
  Running ‘devCritFun.R’ [2s/4s]
  Running ‘devfunQ.R’ [2s/3s]
  Running ‘drop.R’ [3s/4s]
  Running ‘drop1contrasts.R’ [2s/4s]
  Running ‘dynload.R’ [0s/1s]
  Running ‘elston.R’ [3s/3s]
  Running ‘evalCall.R’ [2s/3s]
  Running ‘extras.R’ [2s/3s]
  Running ‘falsezero_dorie.R’ [2s/3s]
  Running ‘fewlevels.R’ [11s/15s]
  Running ‘getME.R’ [3s/4s]
  Running ‘glmer-1.R’ [4s/6s]
  Running ‘glmerControlPass.R’ [7s/12s]
  Running ‘glmerWarn.R’ [2s/3s]
  Running ‘glmmExt.R’ [7s/11s]
  Running ‘glmmWeights.R’ [6s/9s]
  Running ‘hatvalues.R’ [2s/4s]
  Running ‘is.R’ [2s/4s]
  Running ‘lmList-tst.R’ [2s/4s]
  Running ‘lme4_nlme.R’ [2s/4s]
  Running ‘lmer-0.R’ [2s/3s]
  Running ‘lmer-1.R’ [4s/6s]
  Comparing ‘lmer-1.Rout’ to ‘lmer-1.Rout.save’ ... OK
  Running ‘lmer-conv.R’ [2s/3s]
  Running ‘lmer2_ex.R’ [2s/3s]
  Running ‘methods.R’ [2s/4s]
  Running ‘minval.R’ [2s/3s]
  Running ‘modFormula.R’ [2s/4s]
  Running ‘nbinom.R’ [2s/3s]
  Running ‘nlmer-conv.R’ [2s/3s]
  Running ‘nlmer.R’ [2s/3s]
  Comparing ‘nlmer.Rout’ to ‘nlmer.Rout.save’ ... OK
  Running ‘offset.R’ [3s/5s]
  Running ‘optimizer.R’ [4s/7s]
  Running ‘polytomous.R’ [2s/2s]
  Running ‘prLogistic.R’ [2s/3s]
  Running ‘predict_basis.R’ [3s/4s]
  Running ‘predsim.R’ [3s/4s]
  Running ‘priorWeights.R’ [6s/9s]
  Running ‘priorWeightsModComp.R’ [4s/6s]
  Running ‘profile-tst.R’ [20s/39s]
  Running ‘refit.R’ [19s/30s]
  Running ‘resids.R’ [2s/5s]
  Running ‘respiratory.R’ [7s/12s]
  Running ‘simulate.R’ [5s/7s]
  Running ‘testOptControl.R’ [2s/4s]
  Running ‘test_times.R’ [2s/3s]
  Running ‘testcolonizer.R’ [2s/4s]
  Running ‘testcrab.R’ [8s/11s]
  Running ‘throw.R’ [5s/7s]
  Running ‘utils.R’ [0s/1s]
  Running ‘varcorr.R’ [3s/5s]
  Running ‘vcov-etc.R’ [2s/4s]
Running the tests in ‘tests/AAAtest-all.R’ failed.
Complete output:
  > if(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" )
  + }
  bobyqa : [OK]
  Nelder_Mead : [OK]
  nlminbwrap : [OK]
  nmkbw : [OK]
  optimx.L-BFGS-B : [OK]
  nloptwrap.NLOPT_LN_NELDERMEAD : [OK]
  nloptwrap.NLOPT_LN_BOBYQA : [OK]
  original model:
  cbind(incidence, size - incidence) ~ (1 | herd) + period 
  data:  cbpp 
  optimizers (7): bobyqa, Nelder_Mead, nlminbwrap, nmkbw, optimx.L-BFGS-B,nloptwrap.NLOPT_LN_N...
  1 optimizer(s) failed
  differences in negative log-likelihoods:
  max= 4.61e-07 ; std dev= 1.9e-07 
  ── 1. Failure: simulated (@test-lmList.R#52)  ──────────────────────────────────
  coef(fm3.1) not equal to structure(...).
  Component "(Intercept)": Mean relative difference: 0.03123988
  
  ── 2. Failure: simulated (@test-lmList.R#69)  ──────────────────────────────────
  unname(getDataPart(signif(drop(cf32), 6))) not equal to cbind(...).
  10/10 mismatches (average diff: 0.333)
  [1] -0.4755 - -0.4000 == -0.0755
  [2] -1.0777 - -0.3115 == -0.7663
  [3] -0.7568 - -1.0777 ==  0.3209
  [4] -1.1212 - -0.8411 == -0.2802
  [5] -0.5201 - -0.2738 == -0.2462
  [6]  0.7941 -  0.7432 ==  0.0509
  [7]  0.0723 -  0.7685 == -0.6962
  [8]  0.3534 -  0.0723 ==  0.2810
  [9]  0.0934 -  0.2744 == -0.1810
  ...
  
  ── 3. Failure: lmer (@test-lmer.R#232)  ────────────────────────────────────────
  unname(unlist(getME(fm_noopt, c("theta", "beta")))) not equal to c(0.244179074357121, -0.0336616441209862).
  2/2 mismatches (average diff: 0.052)
  [1]  0.1452 -  0.2442 == -0.09894
  [2] -0.0387 - -0.0337 == -0.00509
  
  ── 4. Failure: coef_lmer (@test-lmer.R#271)  ───────────────────────────────────
  fixef(mix1) not equal to setNames(...).
  10/10 mismatches (average diff: 0.243)
  [1]  0.5856 -  0.270 ==  0.3152
  [2]  0.4655 -  0.383 ==  0.0822
  [3]  0.3549 -  0.451 == -0.0964
  [4]  0.3806 -  0.653 == -0.2723
  [5]  0.5784 -  0.611 == -0.0326
  [6] -0.1567 -  0.495 == -0.6516
  [7]  0.0931 -  0.122 == -0.0291
  [8]  0.3903 -  0.087 ==  0.3033
  [9]  0.2849 - -0.286 ==  0.5706
  ...
  
  ── 5. Failure: bootMer (@test-methods.R#178)  ──────────────────────────────────
  sd(boo01.sp$t) not equal to 8.215586.
  1/1 mismatches
  [1] 7.44 - 8.22 == -0.779
  
  ── 6. Failure: bootMer (@test-methods.R#181)  ──────────────────────────────────
  c(...) not equal to c(243.7551, 256.9104).
  2/2 mismatches (average diff: 11.9)
  [1] 233 - 244 == -10.8
  [2] 270 - 257 ==  13.0
  
  ── 7. Failure: only look for columns that exist in re.form (@test-predict.R#257)
  unname(predict(mm2, n11, type = "response", re.form = ~(1 | School/Rank))) not equal to 0.1174628.
  1/1 mismatches
  [1] 0.249 - 0.117 == 0.131
  
  ── 8. Failure: only look for columns that exist in re.form (@test-predict.R#263)
  unname(predict(mm3, n12, type = "response")) not equal to 0.1832894.
  1/1 mismatches
  [1] 0.126 - 0.183 == -0.0568
  
  ── 9. Failure: only look for columns that exist in re.form (@test-predict.R#283)
  unname(predict(mm4, n11, type = "response")) not equal to 0.2675081.
  1/1 mismatches
  [1] 0.377 - 0.268 == 0.11
  
  ── 10. Failure: Var-Cov factor conversions (@test-utils.R#23)  ─────────────────
  unname(v1) not equal to structure(c(9, 12, 15, 65, 34, 93), clen = 3).
  6/6 mismatches (average diff: 35.2)
  [1] 81 -  9 ==  72
  [2] 36 - 12 ==  24
  [3] 63 - 15 ==  48
  [4] 17 - 65 == -48
  [5] 30 - 34 ==  -4
  [6] 78 - 93 == -15
  
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 777 SKIPPED: 0 FAILED: 10
  1.  Failure: simulated (@test-lmList.R#52) 
  2.  Failure: simulated (@test-lmList.R#69) 
  3.  Failure: lmer (@test-lmer.R#232) 
  4.  Failure: coef_lmer (@test-lmer.R#271) 
  5.  Failure: bootMer (@test-methods.R#178) 
  6.  Failure: bootMer (@test-methods.R#181) 
  7.  Failure: only look for columns that exist in re.form (@test-predict.R#257) 
  8.  Failure: only look for columns that exist in re.form (@test-predict.R#263) 
  9.  Failure: only look for columns that exist in re.form (@test-predict.R#283) 
  10. Failure: Var-Cov factor conversions (@test-utils.R#23) 
  
  Error: testthat unit tests failed
  Execution halted
Running the tests in ‘tests/lmList-tst.R’ failed.
Complete output:
  > library(lme4)
  Loading required package: Matrix
  > 
  > options(nwarnings = 1000)
  > set.seed(17)
  > fm1. <- lmList(Reaction ~ Days | Subject, sleepstudy, pool=FALSE)
  > fm1  <- lmList(Reaction ~ Days | Subject, sleepstudy)
  > ## coef(fm1)
  > 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))
  > stopifnot(all.equal(signif(coef(fm1), 8), cf.fm1,
  + 		    tolerance = 1e-7, check.attributes=FALSE),
  + 	  all.equal(coef(fm1.), coef(fm1), tolerance = 1e-15),
  + 	  inherits(formula(fm1), "formula") # <- had been wrong till 2015-04-09
  + 	  )
  > 
  > sm1. <- summary(fm1.)
  > sm1 <- summary(fm1)
  > iC <- which(names(sm1) %in% c("call", "pool"       # << differ
  +                             , "coefficients"       # << SE, z, P differ
  +                             , "df.residual", "RSE" # << exist only when pooled
  +             ))
  > stopifnot(all.equal(sm1$RSE, 25.5918156267, tolerance = 1e-10),
  +           sm1$df.residual == 144,
  +           all.equal(sm1[-iC], sm1.[-iC],    tolerance = 1e-10))
  > cf1 <- confint(fm1)
  > 
  > if(!dev.interactive(orNone=TRUE)) pdf("lmList_plots.pdf")
  > 
  > ## Calling the plot.lmList4.confint() method :
  > stopifnot(inherits(pcf1 <- plot(cf1), "trellis"))
  > pcf1 # nice lattice plot
  > 
  > data(Orthodont, package="nlme")
  > Orthodont <- as.data.frame(Orthodont) # no "groupedData"
  > fm2 <- lmList(distance ~ age | Subject, Orthodont)
  > coef(fm2)
      (Intercept)   age
  M16       16.95 0.550
  M05       13.65 0.850
  M02       14.85 0.775
  M11       20.05 0.325
  M07       14.95 0.800
  M08       19.75 0.375
  M03       16.00 0.750
  M12       13.25 1.000
  M13        2.80 1.950
  M14       19.10 0.525
  M09       14.40 0.975
  M15       13.50 1.125
  M06       18.95 0.675
  M04       24.70 0.175
  M01       17.30 0.950
  M10       21.25 0.750
  F10       13.55 0.450
  F09       18.10 0.275
  F06       17.00 0.375
  F01       17.25 0.375
  F05       19.60 0.275
  F07       16.95 0.550
  F02       14.20 0.800
  F08       21.45 0.175
  F03       14.40 0.850
  F04       19.65 0.475
  F11       18.95 0.675
  > (fe2 <- fixef(fm2))# did fail, now fine
  (Intercept)         age 
   16.7611111   0.6601852 
  > stopifnot(all.equal(fe2, c("(Intercept)" = 16.7611111111111,
  +                            age = 0.660185185185185)))
  > stopifnot(inherits(print(pairs(fm2)), "trellis"))
  > ##                       ----- needs a non-trivial X matrix
  > 
  > 
  > 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)
  > coef(fm3.1)
    (Intercept)
  A    47.51439
  B    51.09276
  C    49.15102
  D    48.47297
  E    49.51207
  > (cf31 <- confint(fm3.1))
  An object of class "lmList4.confint"
  , , (Intercept)
  
       2.5 %   97.5 %
  A 38.75676 56.27201
  B 42.85972 59.32581
  C 41.26011 57.04193
  D 39.94771 56.99823
  E 42.55202 56.47213
  
  > stopifnot(inherits(print(plot(cf31)), "trellis"))
  > 
  > fm3.2 <- lmList(y2 ~ 1 | g, data=d, family=binomial)
  > (cf32 <- confint(fm3.2)) #                 ^^^^^^^^ "glmList"
  Waiting for profiling to be done...
  Waiting for profiling to be done...
  Waiting for profiling to be done...
  Waiting for profiling to be done...
  Waiting for profiling to be done...
  Waiting for profiling to be done...
  An object of class "lmList4.confint"
  , , (Intercept)
  
         2.5 %     97.5 %
  A -0.4755116 0.79408887
  B -1.0777370 0.07231379
  C -0.7568118 0.35336010
  D -1.1212370 0.09336921
  E -0.5200577 0.45729275
  
  > stopifnot(identical(dim(cf32), c(5L,2:1)),
  + 	  inherits(print(plot(cf32)), "trellis"),
  + 	  all.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))))
  Error: unname(getDataPart(signif(drop(cf32), 6))) and cbind(c(-0.400041, -0.311489, -1.07774, -0.841075, -0.273828),  .... are not equal:
    Mean relative difference: 0.5822903
  Execution halted
Running the tests in ‘tests/lmer-0.R’ failed.
Complete output:
  > require(lme4)
  Loading required package: lme4
  Loading required package: Matrix
  > source(system.file("test-tools-1.R", package = "Matrix"))# identical3() etc
  Loading required package: tools
  > 
  > ## 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(unname(fixef(fm)), -0.8345, tolerance=.01))
  Error: unname(fixef(fm)) and -0.8345 are not equal:
    Mean relative difference: 0.2830076
  Execution halted
Running the tests in ‘tests/priorWeights.R’ failed.
Complete output:
  > 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))
  + }
  > 
  > 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")
  Loading required package: Matrix
  
  Attaching package: 'lme4'
  
  The following object is masked from 'package:nlme':
  
      lmList
  
  > 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))
  Warning message:
  In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
    Model failed to converge with max|grad| = 0.00410739 (tol = 0.002, component 1)
  > 
  > 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)
                                       ML1 
  "Mean relative difference: 0.0007692551" 
                                     REML1 
   "Mean relative difference: 9.81152e-08" 
                                       ML2 
  "Mean relative difference: 0.0005681334" 
                                     REML2 
  "Mean relative difference: 4.612019e-07" 
                                       ML1 
   "Mean relative difference: 0.001098226" 
                                     REML2 
  "Mean relative difference: 2.031984e-05" 
  > 
  > ## 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$tolerance <- 0
  > do.call(all.equal, sleepComp)
  [1] "Mean relative difference: 0.0008574269"
  > 
  > if (require("mlmRev")) {
  +     n <- nrow(Chem97)
  +     v <- rpois(n,1) + 1
  +     w <- 1/v
  +     Chem97Lme <- lme(score ~ 1, random = ~ 1|lea/school, Chem97)
  +     Chem97Lmer <- lmer(score ~ (1|lea/school), Chem97)
  +     Chem97Comp <- compFunc(Chem97Lme, Chem97Lmer)
  +     stopifnot(do.call(all.equal, Chem97Comp))
  +     ## look at relative differences:
  +     Chem97Comp$tolerance <- 0
  +     do.call(all.equal, Chem97Comp)
  + }
  Loading required package: mlmRev
  
  Attaching package: 'mlmRev'
  
  The following objects are masked from 'package:nlme':
  
      Oxboys, bdf
  
  [1] "Mean relative difference: 1.752437e-05"
  > 
  > 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))
  Error: unname(fixef(m)) and fixef_lme4.0 are not equal:
    Mean relative difference: 1.514246
  Execution halted