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. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.