Source: lme4, r-cran-mlmrev
Control: found -1 lme4/1.1-20-1
Control: found -1 r-cran-mlmrev/1.0-6-5
Severity: important
X-Debbugs-CC: debian-ci@lists.debian.org
User: debian-ci@lists.debian.org
Usertags: breaks needs-update
Dear maintainers,
With a recent upload of lme4 the autopkgtest of r-cran-mlmrev fails in
testing when that autopkgtest is run with the binary packages of lme4
from unstable. It passes when run with only packages from testing. In
tabular form:
pass fail
lme4 from testing 1.1-20-1
r-cran-mlmrev from testing 1.0-6-5
all others from testing from testing
I copied some of the output at the bottom of this report.
Currently this regression is blocking the migration of lme4 to testing
[1]. Due to the nature of this issue, I filed this bug report against
both packages. Can you please investigate the situation and reassign the
bug to the right package? If needed, please change the bug's severity.
More information about this bug and the reason for filing it can be found on
https://wiki.debian.org/ContinuousIntegration/RegressionEmailInformation
Paul
[1] https://qa.debian.org/excuses.php?package=lme4
https://ci.debian.net/data/autopkgtest/testing/amd64/r/r-cran-mlmrev/1896877/log.gz
autopkgtest [04:42:02]: test run-unit-test: [-----------------------
R version 3.5.2 (2018-12-20) -- "Eggshell Igloo"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(mlmRev)
Loading required package: lme4
Loading required package: Matrix
> options(digits=6, useFancyQuotes = FALSE)# signif.stars for once..
> fm <- glmer(immun ~ kid2p + mom25p + ord + ethn + momEd +
+ husEd + momWork + rural + pcInd81 + (1|mom) + (1|comm),
+ data = guImmun, family = binomial)
Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :>
Model failed to converge with max|grad| = 0.379791 (tol = 0.001,
component 1)
print(fm, symbolic.cor = TRUE)
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: immun ~ kid2p + mom25p + ord + ethn + momEd + husEd + momWork +
rural + pcInd81 + (1 | mom) + (1 | comm)
Data: guImmun
AIC BIC logLik deviance df.resid
2747.50 2849.69 -1355.75 2711.50 2141
Random effects:
Groups Name Std.Dev.
mom (Intercept) 1.149
comm (Intercept) 0.726
Number of obs: 2159, groups: mom, 1595; comm, 161
Fixed Effects:
(Intercept) kid2pY mom25pY ord23 ord46
ord7p
-0.87528 1.26566 -0.12819 -0.14590 0.16263
0.27387
ethnN ethnS momEdP momEdS husEdP
husEdS
-0.16411 -0.06192 0.28204 0.27803 0.38571
0.35188
husEdU momWorkY ruralY pcInd81
-0.00285 0.25940 -0.67711 -0.85380
convergence code 0; 1 optimizer warnings; 0 lme4 warnings
>
> fm.h <- update(fm, ~ . - husEd)
Warning message:
> In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.468307 (tol = 0.001,
component 1)
print(fm.h, corr = FALSE)
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: immun ~ kid2p + mom25p + ord + ethn + momEd + momWork + rural +
pcInd81 + (1 | mom) + (1 | comm)
Data: guImmun
AIC BIC logLik deviance df.resid
2748.60 2833.76 -1359.30 2718.60 2144
Random effects:
Groups Name Std.Dev.
mom (Intercept) 1.119
comm (Intercept) 0.723
Number of obs: 2159, groups: mom, 1595; comm, 161
Fixed Effects:
(Intercept) kid2pY mom25pY ord23 ord46
ord7p
-0.609 1.261 -0.147 -0.149 0.151
0.230
ethnN ethnS momEdP momEdS momWorkY
ruralY
-0.246 -0.101 0.337 0.352 0.267
-0.715
pcInd81
-0.824
convergence code 0; 1 optimizer warnings; 0 lme4 warnings
> fm.ho <- update(fm.h, ~ . - ord)
Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.194562 (tol = 0.001,
component 1)
> ## FIXME: shows 53 outer iterations (+ probably IRLS ones) --
> ## but no such info is kept stored
> print(fm.ho, corr = FALSE)
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: immun ~ kid2p + mom25p + ethn + momEd + momWork + rural +
pcInd81 +
(1 | mom) + (1 | comm)
Data: guImmun
AIC BIC logLik deviance df.resid
2746.30 2814.43 -1361.15 2722.30 2147
Random effects:
Groups Name Std.Dev.
mom (Intercept) 1.111
comm (Intercept) 0.711
Number of obs: 2159, groups: mom, 1595; comm, 161
Fixed Effects:
(Intercept) kid2pY mom25pY ethnN ethnS
momEdP
-0.6808 1.2644 0.0262 -0.2113 -0.0792
0.3306
momEdS momWorkY ruralY pcInd81
0.3229 0.2487 -0.6803 -0.8419
convergence code 0; 1 optimizer warnings; 0 lme4 warnings
>
> anova(fm, fm.h, fm.ho)
Data: guImmun
Models:
fm.ho: immun ~ kid2p + mom25p + ethn + momEd + momWork + rural + pcInd81 +
fm.ho: (1 | mom) + (1 | comm)
fm.h: immun ~ kid2p + mom25p + ord + ethn + momEd + momWork + rural +
fm.h: pcInd81 + (1 | mom) + (1 | comm)
fm: immun ~ kid2p + mom25p + ord + ethn + momEd + husEd + momWork +
fm: rural + pcInd81 + (1 | mom) + (1 | comm)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
fm.ho 12 2746 2814 -1361 2722
fm.h 15 2749 2834 -1359 2719 3.701 3 0.2956
fm 18 2748 2850 -1356 2712 7.100 3 0.0688 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> (fm.hoe <- update(fm.ho, ~ . - ethn))
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: immun ~ kid2p + mom25p + momEd + momWork + rural + pcInd81 +
(1 | mom) + (1 | comm)
Data: guImmun
AIC BIC logLik deviance df.resid
2742.77 2799.54 -1361.38 2722.77 2149
Random effects:
Groups Name Std.Dev.
mom (Intercept) 1.093
comm (Intercept) 0.698
Number of obs: 2159, groups: mom, 1595; comm, 161
Fixed Effects:
(Intercept) kid2pY mom25pY momEdP momEdS
momWorkY
-0.7017 1.2660 0.0252 0.3530 0.3604
0.2588
ruralY pcInd81
-0.6709 -0.9534
Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
:convergence code 0; 1 optimizer warnings; 0 lme4 warnings
Model failed to converge with max|grad| = 0.182133 (tol = 0.001,
component 1)
>
> (fm.hoem <- update(fm.hoe, ~ . - mom25p))
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: immun ~ kid2p + momEd + momWork + rural + pcInd81 + (1 | mom) +
(1 | comm)
Data: guImmun
AIC BIC logLik deviance df.resid
2740.80 2791.89 -1361.40 2722.80 2150
Random effects:
Groups Name Std.Dev.
mom (Intercept) 1.119
comm (Intercept) 0.696
Number of obs: 2159, groups: mom, 1595; comm, 161
Fixed Effects:
(Intercept) kid2pY momEdP momEdS momWorkY
ruralY
-0.699 1.262 0.359 0.361 0.273
-0.671
pcInd81
-0.946
convergence code 0; 1 optimizer warnings; 0 lme4 warnings
Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0604445 (tol = 0.001,
component 1)
>
> (AN <- anova(fm, fm.h, fm.ho, fm.hoe, fm.hoem))
Data: guImmun
Models:
fm.hoem: immun ~ kid2p + momEd + momWork + rural + pcInd81 + (1 | mom) +
fm.hoem: (1 | comm)
fm.hoe: immun ~ kid2p + mom25p + momEd + momWork + rural + pcInd81 +
fm.hoe: (1 | mom) + (1 | comm)
fm.ho: immun ~ kid2p + mom25p + ethn + momEd + momWork + rural + pcInd81 +
fm.ho: (1 | mom) + (1 | comm)
fm.h: immun ~ kid2p + mom25p + ord + ethn + momEd + momWork + rural +
fm.h: pcInd81 + (1 | mom) + (1 | comm)
fm: immun ~ kid2p + mom25p + ord + ethn + momEd + husEd + momWork +
fm: rural + pcInd81 + (1 | mom) + (1 | comm)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
fm.hoem 9 2741 2792 -1361 2723
fm.hoe 10 2743 2800 -1361 2723 0.026 1 0.8708
fm.ho 12 2746 2814 -1361 2722 0.471 2 0.7903
fm.h 15 2749 2834 -1359 2719 3.701 3 0.2956
fm 18 2748 2850 -1356 2712 7.100 3 0.0688 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> AN[, "logLik"] + 1362 # an inversion in the first
two models
[1] 0.601852 0.615079 0.850417 2.701100 6.251044
> ## FIXME: AN doesn't have a deviance column!
> ## AN[, "deviance"] - 2711 # deviance scale shows this
more clearly
> stopifnot(AN[,"Df"] == c(9,10,12,15,18),
+ # all.equal(AN[,"logLik"] + 1362,
+ # c(0.6072186497422, 0.6289103306312, 0.8541186984307,
+ # 2.725550814599, 6.299084917162), tol = 1e-6),
+ # all.equal(fixef(fm.hoem)[-1],
+ # c("kid2pY" = 1.2662536, "momEdP"= 0.35116180,
+ # "momEdS"= 0.3487824136, "momWorkY"=0.2672759992340,
+ # "ruralY"=-0.678846606719,
"pcInd81"=-0.9612710104134),
+ # tol = 1e-4),
+ TRUE
+ )
>
>
> cat('Time elapsed: ', proc.time(),'\n') # "stats"
Time elapsed: 110.268 0.116 110.452 0 0
>
R version 3.5.2 (2018-12-20) -- "Eggshell Igloo"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> #### LMER: Put all the small data set tests into one file
> library(mlmRev)
Loading required package: lme4
Loading required package: Matrix
> options(digits=6, show.signif.stars = FALSE)
>
> ## bdf ---------------- Data ---------------------
> (fm01 <- lmer(langPOST ~ IQ.ver.cen + avg.IQ.ver.cen +
(1|schoolNR), bdf))
Linear mixed model fit by REML ['lmerMod']
Formula: langPOST ~ IQ.ver.cen + avg.IQ.ver.cen + (1 | schoolNR)
Data: bdf
REML criterion at convergence: 15232.2
Random effects:
Groups Name Std.Dev.
schoolNR (Intercept) 2.81
Residual 6.49
Number of obs: 2287, groups: schoolNR, 131
Fixed Effects:
(Intercept) IQ.ver.cen avg.IQ.ver.cen
40.74 2.41 1.59
> (fm02 <- lmer(langPOST ~ IQ.ver.cen + avg.IQ.ver.cen
+(IQ.ver.cen|schoolNR), bdf))
Linear mixed model fit by REML ['lmerMod']
Formula: langPOST ~ IQ.ver.cen + avg.IQ.ver.cen + (IQ.ver.cen | schoolNR)
Data: bdf
REML criterion at convergence: 15217.9
Random effects:
Groups Name Std.Dev. Corr
schoolNR (Intercept) 2.842
IQ.ver.cen 0.456 -0.64
Residual 6.430
Number of obs: 2287, groups: schoolNR, 131
Fixed Effects:
(Intercept) IQ.ver.cen avg.IQ.ver.cen
40.75 2.46 1.41
> ##
> anova(fm01, fm02)
refitting model(s) with ML (instead of REML)
Data: bdf
Models:
fm01: langPOST ~ IQ.ver.cen + avg.IQ.ver.cen + (1 | schoolNR)
fm02: langPOST ~ IQ.ver.cen + avg.IQ.ver.cen + (IQ.ver.cen | schoolNR)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
fm01 5 15238 15266 -7614 15228
fm02 7 15228 15268 -7607 15214 14.01 2 0.000905
> cat('Time elapsed: ', (.pt <- proc.time()),'\n') # "stats"
Time elapsed: 1.436 0.1 1.533 0 0
>
> ## egsingle ----------- Data ---------------------
> (fm1 <- lmer(math ~ year + (1|childid) + (1|schoolid), egsingle))
Linear mixed model fit by REML ['lmerMod']
Formula: math ~ year + (1 | childid) + (1 | schoolid)
Data: egsingle
REML criterion at convergence: 16759.4
Random effects:
Groups Name Std.Dev.
childid (Intercept) 0.818
schoolid (Intercept) 0.432
Residual 0.589
Number of obs: 7230, groups: childid, 1721; schoolid, 60
Fixed Effects:
(Intercept) year
-0.780 0.746
> (fm2 <- lmer(math ~ year + (1|childid) + (year|schoolid), egsingle))
Linear mixed model fit by REML ['lmerMod']
Formula: math ~ year + (1 | childid) + (year | schoolid)
Data: egsingle
REML criterion at convergence: 16482.4
Random effects:
Groups Name Std.Dev. Corr
childid (Intercept) 0.820
schoolid (Intercept) 0.408
year 0.108 0.44
Residual 0.570
Number of obs: 7230, groups: childid, 1721; schoolid, 60
Fixed Effects:
(Intercept) year
-0.777 0.763
> (fm3 <- lmer(math ~ year + (year|childid) + (1|schoolid), egsingle))
Linear mixed model fit by REML ['lmerMod']
Formula: math ~ year + (year | childid) + (1 | schoolid)
Data: egsingle
REML criterion at convergence: 16517.5
Random effects:
Groups Name Std.Dev. Corr
childid (Intercept) 0.805
year 0.147 0.46
schoolid (Intercept) 0.392
Residual 0.549
Number of obs: 7230, groups: childid, 1721; schoolid, 60
Fixed Effects:
(Intercept) year
-0.793 0.747
> (fm4 <- lmer(math ~ year + (year|childid) + (year|schoolid), egsingle))
Linear mixed model fit by REML ['lmerMod']
Formula: math ~ year + (year | childid) + (year | schoolid)
Data: egsingle
REML criterion at convergence: 16336.7
Random effects:
Groups Name Std.Dev. Corr
childid (Intercept) 0.800
year 0.106 0.55
schoolid (Intercept) 0.411
year 0.106 0.40
Residual 0.549
Number of obs: 7230, groups: childid, 1721; schoolid, 60
Fixed Effects:
(Intercept) year
-0.779 0.763
> ##
> anova(fm1, fm2, fm3, fm4)
refitting model(s) with ML (instead of REML)
Data: egsingle
Models:
fm1: math ~ year + (1 | childid) + (1 | schoolid)
fm2: math ~ year + (1 | childid) + (year | schoolid)
fm3: math ~ year + (year | childid) + (1 | schoolid)
fm4: math ~ year + (year | childid) + (year | schoolid)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
fm1 5 16757 16791 -8374 16747
fm2 7 16486 16534 -8236 16472 275.2 2 <2e-16
fm3 7 16519 16567 -8253 16505 0.0 0 1
fm4 9 16344 16406 -8163 16326 179.1 2 <2e-16
> cat('Time elapsed: ', {.ot <- .pt; (.pt <- proc.time()) - .ot},'\n') #
"stats"
Time elapsed: 2.856 0.012 2.869 0 0
>
> ## Early -------------- Data ---------------------
> Early$tos <- Early$age - 0.5
> (fm1E <- lmer(cog ~ tos * trt + (tos|id), Early))
singular fit
Linear mixed model fit by REML ['lmerMod']
Formula: cog ~ tos * trt + (tos | id)
Data: Early
REML criterion at convergence: 2358.74
Random effects:
Groups Name Std.Dev. Corr
id (Intercept) 12.86
tos 3.21 -1.00
Residual 8.69
Number of obs: 309, groups: id, 103
Fixed Effects:
(Intercept) tos trtY tos:trtY
118.41 -21.13 4.22 5.27
convergence code 0; 1 optimizer warnings; 0 lme4 warnings
>
> ## Exam --------------- Data ---------------------
> (fm05 <- lmer(normexam ~ standLRT + sex + schgend + (1|school), Exam))
Linear mixed model fit by REML ['lmerMod']
Formula: normexam ~ standLRT + sex + schgend + (1 | school)
Data: Exam
REML criterion at convergence: 9347.67
Random effects:
Groups Name Std.Dev.
school (Intercept) 0.293
Residual 0.750
Number of obs: 4059, groups: school, 65
Fixed Effects:
(Intercept) standLRT sexM schgendboys schgendgirls
-0.00105 0.55975 -0.16739 0.17769 0.15900
>
> ## Chem97 ------------- Data ---------------------
> (fm06 <- lmer(score ~ gcsecnt + (1|school) + (1|lea), Chem97))
Linear mixed model fit by REML ['lmerMod']
Formula: score ~ gcsecnt + (1 | school) + (1 | lea)
Data: Chem97
REML criterion at convergence: 141697
Random effects:
Groups Name Std.Dev.
school (Intercept) 1.080
lea (Intercept) 0.122
Residual 2.270
Number of obs: 31022, groups: school, 2410; lea, 131
Fixed Effects:
(Intercept) gcsecnt
5.64 2.47
>
> cat('Time elapsed: ', {.ot <- .pt; (.pt <- proc.time()) - .ot},'\n') #
"stats"
Time elapsed: 0.56 0.012 0.573 0 0
>
> ## Hsb82 -------------- Data ---------------------
> lmer(mAch ~ meanses*cses + sector*cses + (cses|school), Hsb82)
Linear mixed model fit by REML ['lmerMod']
Formula: mAch ~ meanses * cses + sector * cses + (cses | school)
Data: Hsb82
REML criterion at convergence: 46503.7
Random effects:
Groups Name Std.Dev. Corr
school (Intercept) 1.543
cses 0.318 0.39
Residual 6.060
Number of obs: 7185, groups: school, 160
Fixed Effects:
(Intercept) meanses cses
12.13 5.33 2.95
sectorCatholic meanses:cses cses:sectorCatholic
1.23 1.04 -1.64
>
> ## Oxford ------------- Data ---------------------
> (fm07 <- lmer(height ~ age + I(age^2) + I(age^3) + I(age^4) +
+ (age + I(age^2)|Subject), data = Oxboys))
Linear mixed model fit by REML ['lmerMod']
Formula: height ~ age + I(age^2) + I(age^3) + I(age^4) + (age + I(age^2) |
Subject)
Data: Oxboys
REML criterion at convergence: 627.908
Random effects:
Groups Name Std.Dev. Corr
Subject (Intercept) 8.003
age 1.693 0.61
I(age^2) 0.821 0.22 0.66
Residual 0.466
Number of obs: 234, groups: Subject, 26
Fixed Effects:
(Intercept) age I(age^2) I(age^3) I(age^4)
149.019 6.174 1.128 0.454 -0.377
convergence code 0; 1 optimizer warnings; 0 lme4 warnings
Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.00425429 (tol = 0.002,
component 1)
> (fm08 <- lmer(height ~ poly(age,4) +
+ (age + I(age^2)|Subject), data = Oxboys))
Linear mixed model fit by REML ['lmerMod']
Formula: height ~ poly(age, 4) + (age + I(age^2) | Subject)
Data: Oxboys
REML criterion at convergence: 616.869
Random effects:
Groups Name Std.Dev. Corr
Subject (Intercept) 8.004
age 1.692 0.61
I(age^2) 0.821 0.22 0.66
Residual 0.466
Number of obs: 234, groups: Subject, 26
Fixed Effects:
(Intercept) poly(age, 4)1 poly(age, 4)2 poly(age, 4)3 poly(age, 4)4
149.520 64.541 4.203 1.291 -0.585
> anova(fm07, fm08)
refitting model(s) with ML (instead of REML)
Data: Oxboys
Models:
fm07: height ~ age + I(age^2) + I(age^3) + I(age^4) + (age + I(age^2) |
fm07: Subject)
fm08: height ~ poly(age, 4) + (age + I(age^2) | Subject)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
fm07 12 649.3 690.8 -312.7 625.3
fm08 12 649.3 690.8 -312.7 625.3 0 0 1
> stopifnot(all.equal(logLik(fm07, REML=FALSE),
+ logLik(fm08, REML=FALSE), tol=1e-07))
Error: logLik(fm07, REML = FALSE) and logLik(fm08, REML = FALSE) are not
equal:
Mean relative difference: 4.53445e-07
Execution halted
autopkgtest [04:43:59]: test run-unit-test: -----------------------]
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