[R] False convergence of a glmer model

[Douglas Bates]

On Tue, Feb 16, 2010 at 9:05 AM, Shige Song <shigesong at gmail.com> wrote:
> 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)"

That message means that the optimizer function, nlminb, got stalled.
It has converged but the point at which is has converged is not
clearly the optimum.  In many cases this just indicates that the
optimizer is being overly cautious.  However, it can also mean that
the problem is ill-defined.

The fact the the second parameter is -7.46 is likely the problem.  A
difference in the probability of infant mortality between levels of
cohort on the order of -7.5 on the logit scale is huge.   Do the
estimated probabilities at this value of the parameters make sense?

P.S. Questions of this sort may be more readily answered in the
R-SIG-mixed-models mailing list.

> 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.
>
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