[R] False convergence of a glmer model

[Shige Song]

Dear All,

I am trying to fit a 2-level random intercept logistic regression on a
data set of 20,000 cases.  The model is specified as the following:

 m1 <- glmer(inftmort ~ as.factor(cohort) + (1|code), family=binomial, data=d)

I got "Warning message: In mer_finalize(ans) : false convergence (8)"

With the "verbose=TRUE" option, I was able to get the following
iteration history:

  0:     3456.4146:  1.15161 -3.99068 -0.498790 -0.122116
  1:     3361.3370:  1.04044 -4.38172 -0.561756 -0.289991
  2:     3303.7986:  1.48296 -4.40741 -0.566208 -0.259730
  3:     3147.5537:  1.93037 -5.14388 -0.682530 -0.443006
  4:     3123.6900:  2.10192 -5.18784 -0.685558 -0.428320
  5:     2988.6287:  2.94890 -6.31023 -0.825286 -0.586282
  6:     2958.3364:  3.25396 -6.88256 -0.316988 0.572428
  7:     2853.7703:  4.22731 -7.44955 -0.279492 -0.294353
  8:     2844.8476:  4.36583 -7.43902 -0.293111 -0.267308
  9:     2843.2879:  4.39182 -7.44895 -0.298791 -0.265899
 10:     2840.2676:  4.44288 -7.47103 -0.310477 -0.263945
 11:     2839.0890:  4.46259 -7.48131 -0.315320 -0.263753
 12:     2838.8550:  4.46649 -7.48344 -0.316292 -0.263745
 13:     2838.3889:  4.47428 -7.48771 -0.318236 -0.263737
 14:     2838.3703:  4.47459 -7.48788 -0.318314 -0.263738
 15:     2838.2216:  4.47708 -7.48927 -0.318936 -0.263742
 16:     2838.2157:  4.47718 -7.48932 -0.318961 -0.263742
 17:     2838.2145:  4.47720 -7.48934 -0.318966 -0.263742
 18:     2838.2121:  4.47724 -7.48936 -0.318976 -0.263742
 19:     2838.2120:  4.47724 -7.48936 -0.318976 -0.263742
 20:     2838.2118:  4.47724 -7.48936 -0.318977 -0.263742
 21:     2838.2118:  4.47724 -7.48936 -0.318977 -0.263742
 22:     2838.2118:  4.47724 -7.48936 -0.318977 -0.263742
 23:     2838.2118:  4.47724 -7.48936 -0.318977 -0.263742
 24:     2838.2118:  4.47724 -7.48936 -0.318977 -0.263742
 25:     2838.2118:  4.47724 -7.48936 -0.318977 -0.263742
 26:     2838.2118:  4.47724 -7.48936 -0.318977 -0.263742
 27:     2838.2118:  4.47724 -7.48936 -0.318977 -0.263742
 28:     2838.2118:  4.47724 -7.48936 -0.318977 -0.263742
 29:     2838.2118:  4.47724 -7.48936 -0.318977 -0.263742
 30:     2838.2118:  4.47724 -7.48936 -0.318977 -0.263742
 31:     2838.2118:  4.47724 -7.48936 -0.318977 -0.263742
 32:     2838.2118:  4.47724 -7.48936 -0.318977 -0.263742
 33:     2837.8154:  4.46385 -7.47464 -0.495684 -0.263985
 34:     2837.7613:  4.46641 -7.47053 -0.498335 -0.264014
 35:     2837.6418:  4.47259 -7.46200 -0.501644 -0.264141
 36:     2837.5982:  4.47485 -7.45928 -0.502598 -0.264214
 37:     2837.5850:  4.47537 -7.45882 -0.502848 -0.264237
 38:     2837.5307:  4.47674 -7.45848 -0.503216 -0.264313
 39:     2837.5014:  4.47725 -7.45875 -0.503273 -0.264344
 40:     2837.4955:  4.47735 -7.45881 -0.503284 -0.264350
 41:     2837.4944:  4.47738 -7.45882 -0.503286 -0.264351
 42:     2837.4941:  4.47738 -7.45882 -0.503287 -0.264351
 43:     2837.4936:  4.47739 -7.45883 -0.503288 -0.264352
 44:     2837.4935:  4.47739 -7.45883 -0.503288 -0.264352
 45:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 46:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 47:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 48:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 49:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 50:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 51:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 52:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 53:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 54:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 55:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 56:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 57:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 58:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 59:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 60:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 61:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 62:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352
 63:     2837.4931:  4.47740 -7.45883 -0.503289 -0.264352

By the way, the same model can be fitted using Stata using xtlogit and
xtmelogit; a simpler model without the random component can be
estimated using R as:

m <- glm(inftmort ~ as.factor(cohort), family=binomial, data=d)

I was also able to get highly consistent results via MCMC simulation
using MCMCglmm.

It will be greatly appreciated if someone can give me some hints where
to look further. Thanks.

Best,
Shige

BTW, sorry about the earlier post, which was caused by a mistake.