Nothing
library("testthat")
library("lme4")
(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", "deviance", "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$y <- suppressMessages(simulate(~1 + (A|B),
newdata=dd,
newparams=list(beta=1,theta=rep(1,6),
sigma=1),
family=gaussian,
seed=101))[[1]]
m1 <- lmer(y ~ 1 + (A|B), data=dd, control=lmerControl(calc.deriv=FALSE))
ci <- CI.boot(m1,seed=101)
ci2 <- CI.boot(m1,seed=101)
expect_equal(ci,ci2)
ci_50 <- CI.boot(m1,level=0.5,seed=101)
expect_true(all(ci_50[,"25 %"]>ci[,"2.5 %"]))
expect_true(all(ci_50[,"75 %"]<ci[,"97.5 %"]))
corvals <- ci[grep("^cor_",rownames(ci)),]
expect_true(all(abs(corvals) <= 1))
## test bootMer with GLMM, multiple RE
## expect_message(
ci1 <- CI.boot(gm2, nsim=5)
## , "singular")
ci2 <- CI.boot(gm2, nsim=5, parm=3:6)
ci2B <- CI.boot(gm2, nsim=5, parm="beta_")
## previously tested with nsim=5 vs nsim=3
expect_true(nrow(ci2) == 4)
expect_equal(ci2, ci2B)
expect_equal(ci1[3:6,], ci2) ## , tolerance = 0.4)# 0.361
## bootMer with NA values
ddNA <- dd
ddNA$A[1:3] <- NA
m2 <- update(m1, data=ddNA)
ci3 <- CI.boot(m2)
expect_equal(ci, ci3, tolerance=0.2)
sleepstudyNA <- sleepstudy
sleepstudyNA$Days[1:3] <- NA
m4 <- update(fm2, data = sleepstudyNA,
control=lmerControl(check.conv.grad = .makeCC("warning",tol=4e-3)))
expect_true(nrow(ci4 <- CI.boot(m4)) == 6) # could check more
##
## semipar bootstrapping
m5 <- lmer(Yield ~ 1|Batch, Dyestuff)
set.seed(1)
suppressPackageStartupMessages(require(boot))
boo01.sp <- bootMer(m5, fixef, nsim = 100, use.u = TRUE,
type = "semiparametric")
expect_equal(sd(boo01.sp$t), 8.215586, tolerance = 1e-4)
## passing FUN to confint: Torbjørn Håkan Ergon
testFun <- function(fit) fixef(fit)[1]
expect_equal(c(unclass(
suppressWarnings(confint(fm1, method="boot", FUN=testFun, nsim=10,
quiet=TRUE)))),
c(243.7551,256.9104),tolerance=1e-3)
## passing re.form to bootMer
## FUN <- function(.) predict(., type="response")
fm3 <- lmer(strength ~ (1|batch/cask), Pastes)
expect_is(bootMer(fm3, predict, nsim=3),"boot")
expect_is(bootMer(fm3, predict, re.form=NULL, nsim=3),"boot")
expect_is(bootMer(fm3, predict, re.form=~(1|batch)+(1|cask:batch), nsim=3),
"boot")
expect_is(b3 <- bootMer(fm3, predict, re.form=~(1|batch), nsim=3),
"boot")
FUN_name <- function(.) getME(.,"theta")
FUN_noname <- function(.) unname(getME(.,"theta"))
c_name <- suppressWarnings(
confint(fm3, method="boot", FUN=FUN_name, nsim=3, seed=101))
c_noname <- suppressWarnings(
confint(fm3, method="boot", FUN=FUN_noname, nsim=3, seed=101))
expect_equal(unname(c_name),unname(c_noname))
## example from @Mark
## bootstrapping etc. on GLMMs with scale
## SO 37466771:
## https://stackoverflow.com/questions/37466771/using-profile-and-boot-method-within-confint-option-with-glmer-model
df2 <- data.frame(
prop1 = c(0.46, 0.471, 0.458, 0.764, 0.742, 0.746,
0.569, 0.45, 0.491, 0.467, 0.464,
0.556, 0.584, 0.478, 0.456, 0.46, 0.493, 0.704, 0.693, 0.651),
prop2 = c(0.458, 0.438, 0.453, 0.731, 0.738, 0.722, 0.613,
0.498, 0.452, 0.451, 0.447,
0.48, 0.576, 0.484, 0.473, 0.467, 0.467, 0.722, 0.707, 0.709),
site = factor(rep(1:8,c(2,1,3,5,3,2,1,3))))
gaussmodel <- glmer(prop2 ~ prop1 + (1|site),
data=df2, family=gaussian(link="logit"))
set.seed(101)
bci <- suppressWarnings(confint(gaussmodel,method="boot",nsim=10,quiet=TRUE))
expect_equal(bci,
structure(c(16.0861072699207, 0.0367496156026639,
-4.21025090053564,
3.1483596407467, 31.3318861915707,
0.0564761126030204, -1.00593089841924,
4.7064432268919), .Dim = c(4L, 2L),
.Dimnames = list(c(".sig01",
".sigma", "(Intercept)",
"prop1"), c("2.5 %", "97.5 %"))),
tolerance=1e-5)
})
} ## testLevel>1
context("confint_other")
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,oldNames=FALSE))
ci1.w.n <- confint(fm1,method="Wald", oldNames=FALSE)
ci1.b.n <- CI.boot(fm1, nsim=2, oldNames=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 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)
if (Sys.info()["sysname"] != "SunOS" &&
.Platform$OS.type != "windows") {
## doesn't produce warnings on Solaris, or win-builder ...
expect_warning(pp <- profile(fit,"theta_"),
"non-monotonic profile")
expect_warning(cc <- confint(pp),"falling back to linear interpolation")
## very small/unstable problem, needs large tolerance
expect_equal(unname(cc[2,]), c(0, 0.509), tolerance=0.09) # "bobyqa" had 0.54276
}
badprof <- readRDS(system.file("testdata","badprof.rds",
package="lme4"))
expect_warning(cc <- confint(badprof), "falling back to linear")
expect_equal(cc,
array(c(0, -1, 2.50856219044636, 48.8305727797906, NA, NA,
33.1204478717389, 1, 7.33374326592662, 68.7254711217912, -6.90462047196017, NA),
dim = c(6L, 2L),
dimnames = list(c(".sig01", ".sig02", ".sig03", ".sigma", "(Intercept)", "cYear"),
c("2.5 %", "97.5 %"))),
tolerance=1e-3)
})
test_that("refit", {
s1 <- simulate(fm1)
expect_is(refit(fm1,s1), "merMod")
s2 <- simulate(fm1,2)
expect_error(refit(fm1,s2), "refit not implemented .* lists")
data(Orthodont,package = "nlme")
fmOrth <- fm <- lmer(distance ~ I(age - 11) + (I(age - 11) | Subject),
data = Orthodont)
expect_equal(s1 <- simulate(fm,newdata = Orthodont,seed = 101),
s2 <- simulate(fm,seed = 101))
## works *without* offset ...
m5 <- glmer(round(Reaction) ~ Days + (1|Subject),
data = sleepstudy, family=poisson,
offset=rep(0,nrow(sleepstudy)))
m5R <- refit(m5)
## lots of fussy details make expect_equal() on the whole object difficult
expect_equal(coef(m5),coef(m5R),tolerance=3e-6)
expect_equal(VarCorr(m5),VarCorr(m5R),tolerance=1e-6)
expect_equal(logLik(m5),logLik(m5R))
})
if (testLevel>1) {
context("predict method")
test_that("predict", {
d1 <- with(cbpp, expand.grid(period = unique(period), herd = unique(herd)))
d2 <- data.frame(period = "1", herd = unique(cbpp$herd))
d3 <- expand.grid(period = as.character(1:3),
herd = unique(cbpp$herd))
p0 <- predict(gm1)
p1 <- predict(gm1,d1)
p2 <- predict(gm1,d2)
p3 <- predict(gm1,d3)
expect_equal(p0[1], p1[1])
expect_equal(p0[1], p2[1])
expect_equal(p0[1], p3[1])
expect_warning(predict(gm1, ReForm=NA), "is deprecated")
## matrix-valued predictors: Github #201 from Fabian S.
sleepstudy$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")
expect_warning(simulate(gm2, ReForm = NA), "is deprecated")
expect_warning(simulate(gm2, REForm = NA), "is deprecated")
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)
expect_warning(p5 <- simulate(gm2, ReForm = ~0, seed = 101), "is deprecated")
p6 <- simulate(gm2, re.form = NA, seed = 101)
expect_warning(p7 <- simulate(gm2, REForm = NULL, seed = 101), "is deprecated")
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,p5)
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,p7)
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$family[["theta"]])# <- important for pkg{effects} eval(<call>)
expect_identical(sum(s1), 413)
expect_identical(as.vector(ts1[as.character(0:5)]),
## c(51L, 54L, 36L, 21L, 14L, 9L))
c(49L,56L,32L,25L,11L,9L))
## de novo NB simulation ...
s2 <- simulate(~x + (1|f),seed=101,
family=MASS::negative.binomial(theta=th.g1),
newparams=getME(g1,c("theta","beta")),
newdata=dd)[,1]
expect_equal(s1,s2)
## Simulate with newdata with *new* RE levels:
d <- sleepstudy[-1] # droping the response ("Reaction")
## d$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)" = 2.81887136759369, 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(i2, i2)) # to see diff
# 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
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.