Running 'guImmun.R' [278s]
  Running 'lmerTest.R' [12s]
  Running 'versions.R' [3s]
Running the tests in 'tests/lmerTest.R' failed.
Complete output:
  > ####  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:  3.18 0.32 3.72 NA NA 
  > 
  > ## 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:  6 0.03 6.1 NA NA 
  > 
  > ## 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:  1.25 0.13 1.38 NA NA 
  > 
  > ## 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) 8.006             
            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| = 0.00267836 (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: 4.51165e-07
  Execution halted