R 3.3.2: lme4 + lmerTest problems under Mac OS Sierra

Question

I've stumbled upon a problem affecting the Mac OS version of R 3.3.2 (and .3 too!) when using lme4 and lmerTest.

lmerTest produces an error:

Error in calculation of the Satterthwaite's approximation. The output of lme4 package is returned summary from lme4 is returned some computational error has occurred in lmerTest

The problem does not emerge with R 3.2 under MacOS and any R versions under Windows. However, this is not an installation problem, since I reproduced the error after reinstall of R and on also on a different Mac.

Here is the example code:

 library(lme4)

#' start of data creation

mydat <- 
  structure(list(ID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 
                        13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 
                        1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 
                        20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 1, 2, 3, 4, 5, 6, 7, 
                        8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 
                        24, 25, 26, 27, 28, 29, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 
                        13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 
                        29, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 
                        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 1, 2, 3, 4, 5, 
                        6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 
                        23, 24, 25, 26, 27, 28, 29), sex = c(1, 1, 1, 1, 1, 1, 1, 1, 
                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), ROI = structure(c(4L, 
                       4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
                       4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 
                       1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
                       1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
                       3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
                       3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                       2L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
                       5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 
                       6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
                       6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("calf", 
                       "DSCAT", "KM", "neck", "SSCAT", "VAT"), class = "factor"), 
                        value = c(0.674, 
                      0.561, 0.543, 0.563, 0.697, 0.608, 0.56, 0.448, 0.626, 0.515, 
                      0.568, 0.528, 0.587, 0.532, 0.547, 0.514, 0.587, 0.572, 0.559, 
                      0.569, 0.462, 0.531, 0.477, 0.582, 0.583, 0.569, 0.563, 0.576, 
                      0.84, 0.638, 0.69, 0.707, 0.704, 0.627, 0.769, 0.637, 0.515, 
                      0.669, 0.699, 0.626, 0.59, 0.639, 0.501, 0.632, 0.624, 0.641, 
                      0.669, 0.656, 0.556, 0.569, 0.633, 0.608, 0.616, 0.664, 0.666, 
                      0.669, 0.545, 0.514, 0.45, 0.585, 0.547, 0.572, 0.577, 0.458, 
                      0.47, 0.537, 0.532, 0.455, 0.62, 0.501, 0.506, 0.44, 0.499, 0.577, 
                      0.457, 0.481, 0.522, 0.516, 0.513, 0.559, 0.571, 0.515, 0.575, 
                      0.521, 0.44, 0.637, 0.521, 0.634, 0.552, 0.581, 0.55, 0.553, 
                      0.522, 0.634, 0.631, 0.512, 0.603, 0.593, 0.58, 0.442, 0.53, 
                      0.463, 0.587, 0.538, 0.48, 0.557, 0.482, 0.53, 0.592, 0.445, 
                      0.526, 0.45, 0.551, 0.51, 0.678, 0.64, 0.599, 0.589, 0.627, 0.621, 
                      0.601, 0.526, 0.619, 0.599, 0.668, 0.615, 0.621, 0.561, 0.532, 
                      0.56, 0.578, 0.686, 0.57, 0.457, 0.563, 0.61, 0.513, 0.638, 0.594, 
                      0.777, 0.562, 0.663, 0.538, 0.471, 0.518, 0.47, 0.535, 0.644, 
                      0.605, 0.474, 0.468, 0.563, 0.539, 0.47, 0.538, 0.453, 0.494, 
                      0.576, 0.418, 0.609, 0.528, 0.453, 0.569, 0.484, 0.486, 0.558, 
                      0.621, 0.465, 0.691, 0.398, 0.539, 0.574), Alter = c(45, 47, 
                     51, 44, 35, 26, 60, 44, 42, 50, 42, 51, 57, 23, 26, 29, 29, 50, 
                     45, 61, 61, 58, 32, 27, 49, 45, 64, 28, 45, 47, 51, 44, 35, 26, 
                     60, 44, 50, 42, 51, 57, 23, 26, 29, 29, 50, 45, 61, 61, 58, 32, 
                     27, 49, 27, 45, 64, 28, 45, 47, 51, 44, 35, 26, 60, 44, 42, 50, 
                     42, 51, 57, 23, 26, 29, 29, 50, 45, 61, 61, 58, 32, 27, 49, 27, 
                     45, 64, 28, 45, 47, 51, 44, 35, 26, 60, 44, 42, 50, 42, 51, 57, 
                     23, 26, 29, 29, 50, 45, 61, 61, 58, 32, 27, 49, 27, 45, 64, 28, 
                     45, 47, 51, 44, 35, 26, 60, 44, 42, 50, 42, 51, 57, 23, 26, 29, 
                     29, 50, 45, 61, 61, 58, 32, 27, 49, 27, 45, 64, 28, 45, 47, 51, 
                     44, 35, 26, 60, 44, 42, 50, 42, 51, 57, 23, 26, 29, 29, 50, 45, 
                     61, 61, 58, 32, 27, 49, 27, 45, 64, 28), 
                      BMI = c(29.7506923675537, 
                  28.8, 28.8385677337646, 41.48, 27.7186069488525, 29.54, 38.06, 
                  35.8453826904297, 35.57, 31.77, 31.75, 32.78, 30.5336246490479, 
                  29.1074104309082, 36.4690246582031, 31.7769088745117, 31.5393238067627, 
                  31.5596752166748, 27.593786239624, 30.8192825317383, 27.0799140930176, 
                  31.481481552124, 29.0328979492188, 24.52, 29.4029197692871, 35.6112785339355, 
                  28.2401905059814, 28.8979587554932, 29.7506923675537, 28.8, 28.8385677337646, 
                  41.48, 27.7186069488525, 29.54, 38.06, 35.8453826904297, 31.77, 
                  31.75, 32.78, 30.5336246490479, 29.1074104309082, 36.4690246582031, 
                  31.7769088745117, 31.5393238067627, 31.5596752166748, 27.593786239624, 
                  30.8192825317383, 27.0799140930176, 31.481481552124, 29.0328979492188, 
                  24.52, 29.4029197692871, 23.0956573486328, 35.6112785339355, 
                  28.2401905059814, 28.8979587554932, 29.7506923675537, 28.8, 28.8385677337646, 
                  41.48, 27.7186069488525, 29.54, 38.06, 35.8453826904297, 35.57, 
                  31.77, 31.75, 32.78, 30.5336246490479, 29.1074104309082, 36.4690246582031, 
                  31.7769088745117, 31.5393238067627, 31.5596752166748, 27.593786239624, 
                  30.8192825317383, 27.0799140930176, 31.481481552124, 29.0328979492188, 
                  24.52, 29.4029197692871, 23.0956573486328, 35.6112785339355, 
                  28.2401905059814, 28.8979587554932, 29.7506923675537, 28.8, 28.8385677337646, 
                  41.48, 27.7186069488525, 29.54, 38.06, 35.8453826904297, 35.57, 
                  31.77, 31.75, 32.78, 30.5336246490479, 29.1074104309082, 36.4690246582031, 
                  31.7769088745117, 31.5393238067627, 31.5596752166748, 27.593786239624, 
                  30.8192825317383, 27.0799140930176, 31.481481552124, 29.0328979492188, 
                  24.52, 29.4029197692871, 23.0956573486328, 35.6112785339355, 
                  28.2401905059814, 28.8979587554932, 29.7506923675537, 28.8, 28.8385677337646, 
                  41.48, 27.7186069488525, 29.54, 38.06, 35.8453826904297, 35.57, 
                  31.77, 31.75, 32.78, 30.5336246490479, 29.1074104309082, 36.4690246582031, 
                  31.7769088745117, 31.5393238067627, 31.5596752166748, 27.593786239624, 
                  30.8192825317383, 27.0799140930176, 31.481481552124, 29.0328979492188, 
                  24.52, 29.4029197692871, 23.0956573486328, 35.6112785339355, 
                  28.2401905059814, 28.8979587554932, 29.7506923675537, 28.8, 28.8385677337646, 
                  41.48, 27.7186069488525, 29.54, 38.06, 35.8453826904297, 35.57, 
                  31.77, 31.75, 32.78, 30.5336246490479, 29.1074104309082, 36.4690246582031, 
                  31.7769088745117, 31.5393238067627, 31.5596752166748, 27.593786239624, 
                  30.8192825317383, 27.0799140930176, 31.481481552124, 29.0328979492188, 
                  24.52, 29.4029197692871, 23.0956573486328, 35.6112785339355, 
                  28.2401905059814, 28.8979587554932)), .Names = c("ID", "sex", 
                  "ROI", "value", "Alter", "BMI"), row.names = c(NA, -172L), class = c("tbl_df","tbl", "data.frame"))

#' end of data creation


library(lmerTest)
mod <- lmer(value~Alter+ROI+BMI+(1|ID),data=mydat,REML=F)
summary(mod)
sessionInfo()

The system information is as follows:

R version 3.3.3 (2017-03-06)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: macOS Sierra 10.12.3

locale:
[1] C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] lmerTest_2.0-33 lme4_1.1-12     Matrix_1.2-8   

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.9         Formula_1.2-1       knitr_1.15.1        magrittr_1.5            cluster_2.0.5       splines_3.3.3       MASS_7.3-45         munsell_0.4.3    [9] colorspace_1.3-2    lattice_0.20-34     minqa_1.2.4         stringr_1.1.0       plyr_1.8.4          tools_3.3.3         nnet_7.3-12         grid_3.3.3    [17] data.table_1.10.0   checkmate_1.8.2     htmlTable_1.8       gtable_0.2.0        nlme_3.1-131        latticeExtra_0.6-28 htmltools_0.3.5     digest_0.6.11    [25] survival_2.40-1     lazyeval_0.2.0      assertthat_0.1      tibble_1.2          gridExtra_2.2.1     RColorBrewer_1.1-2  nloptr_1.0.4        ggplot2_2.2.1    [33] base64enc_0.1-3     acepack_1.4.1       rpart_4.1-10        stringi_1.1.2       backports_1.0.4     scales_0.4.1        Hmisc_4.0-2         foreign_0.8-67

I've had lots of "some computational error has occurred in lmerTest" recently and I have found that at least some of them may have been related to the problem reported here: https://stackoverflow.com/questions/42805643/lmertestanova-uses-lazy-loading-of-data-sets — mmagnuski, Aug 30 '17 at 00:44

score 1 · Answer 1 · answered Mar 14 '17 at 21:18

After a repeated attempt, the code worked under R3.3.3, although my system is unchanged. Was I dreaming? Kind of paranormal... I'm puzzled. Sorry for bothering.

R version 3.3.3 (2017-03-06) Platform: x86_64-apple-darwin13.4.0 (64-bit) Running under: macOS Sierra 10.12.3

locale: [1] C

attached base packages: [1] stats graphics grDevices utils
datasets methods base

other attached packages: [1] lmerTest_2.0-33 lme4_1.1-12
Matrix_1.2-8

loaded via a namespace (and not attached): [1] Rcpp_0.12.9
nloptr_1.0.4 RColorBrewer_1.1-2 plyr_1.8.4
base64enc_0.1-3 tools_3.3.3 rpart_4.1-10
digest_0.6.12 [9] tibble_1.2 nlme_3.1-131
gtable_0.2.0 htmlTable_1.9 checkmate_1.8.2
lattice_0.20-34 gridExtra_2.2.1 stringr_1.2.0 [17] cluster_2.0.5 knitr_1.15.1 htmlwidgets_0.8 grid_3.3.3 nnet_7.3-12 data.table_1.10.0 survival_2.40-1
foreign_0.8-67 [25] latticeExtra_0.6-28 minqa_1.2.4
Formula_1.2-1 ggplot2_2.2.1 magrittr_1.5
Hmisc_4.0-2 scales_0.4.1 backports_1.0.5 [33] htmltools_0.3.5 MASS_7.3-45 splines_3.3.3
assertthat_0.1 colorspace_1.3-2 stringi_1.1.2
acepack_1.4.1 lazyeval_0.2.0 [41] munsell_0.4.3

score 0 · Answer 2 · edited May 23 '17 at 12:00

This isn't really an answer, but it's a bit long for a comment ...

I can't replicate this under either of these environments:

R version 3.3.2 (2016-10-31)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X El Capitan 10.11.6
[1] lmerTest_2.0-33 lme4_1.1-12     Matrix_1.2-8   
(also tried with Matrix 1.2-7)

R Under development (unstable) (2017-02-13 r72168)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.5 LTS
lmerTest_2.0-33 lme4_1.1-13     Matrix_1.2-8

Without replicability it's pretty hard to troubleshoot. A little hard to believe it's Sierra-specific, but stranger things have happened.

I'm going to take a wild guess and suggest that you try downgrading the Matrix package to version 1.2-7 (as described here), although both the symptoms [crash] and suspect platforms [32-bit OS] are different.

Alternatively, you could try digging down into the guts of lmerTest as described here to see what's going on, although again the particular context is different (your model fit is not singular).

CRAN does check packages under 64-bit Sierra, but the checks for lmerTest (and for lme4) are not showing any errors on this platform ...

R 3.3.2: lme4 + lmerTest problems under Mac OS Sierra

2 Answers2