26

我正在尝试使用 lme4 的开发版本和本教程对混合效应模型进行功率分析。我在教程中注意到 lme4 会引发收敛错误：

## Warning: Model failed to converge with max|grad| = 0.00187101 (tol =
## 0.001)

当我为我的数据集运行代码时，会出现同样的警告，其中：

## Warning message: In checkConv(attr(opt, "derivs"), opt$par, checkCtrl =
control$checkConv,  : 
Model failed to converge with max|grad| = 0.774131 (tol = 0.001)

使用此更新版本进行的常规 glmer 调用的估计值也与我使用更新的 CRAN 版本时略有不同（在这种情况下没有警告）。关于为什么会发生这种情况的任何想法？

编辑

我试图指定的模型是：

glmer(resp ~ months.c * similarity * percSem + (similarity | subj), family = binomial, data = myData)

我拥有的数据集有一个主体间（年龄，居中）和两个主体内变量（相似度：2 个级别，percSem：3 个级别），用于预测二元结果（错误记忆/猜测）。此外，每个受试者内部单元格都有 3 个重复测量值。因此，每个人总共有 2 x 3 x 3 = 18 个二元响应，总共有 38 个参与者。

structure(list(subj = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 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, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L), .Label = c("09A", "10", "11", "12", "12A", "13", "14", "14A", "15", "15A", "16", "17", "18", "19", "1A", "2", "20", "21", "22", "22A", "23", "24", "25", "26", "27", "28", "29", "3", "30", "31", "32A", "32B", "33", "4B", "5", "6", "7", "8"), class = "factor"), months.c = structure(c(-9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 2.18421052631579, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, -7.81578947368421, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 9.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, 6.18421052631579, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, -9.81578947368421, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, -6.81578947368421, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 5.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, -1.81578947368421, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, 1.18421052631579, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, -8.81578947368421, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 3.18421052631579, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 11.1842105263158, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, 0.184210526315795, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -4.81578947368421, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -2.81578947368421, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -10.8157894736842, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, -0.815789473684205, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, 8.18421052631579, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421, -3.81578947368421), "`scaled:center`" = 70.8157894736842), similarity = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Dissim", "Sim"), class = "factor"), percSem = structure(c(2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L), .Label = c("Both", "Perc", "Sem"), class = "factor"), resp = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 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, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 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, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,  1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L), .Label = c("false memory", "guess"), class = "factor")), .Names = c("subj", "months.c", "similarity", "percSem", "resp"), row.names = c(NA, -684L), class = "data.frame")
4

1 回答 1

42

tl; dr这看起来像是一个误报——我没有看到各种不同优化器的拟合之间有任何特别重要的差异，尽管看起来异常值是内置的 Nelder-Mead 优化器和 nlminb；内置的 bobyqa 以及 nloptr 包中的 bobyqa 和 Nelder-Mead 给出了非常接近的答案，并且没有警告。

在这些情况下，我的一般建议是尝试重新安装control=glmerControl(optimizer="bobyqa"); 我们正在考虑切换到使用bobyqa作为默认值（这个问题增加了有利于它的证据的权重）。

我将dput输出放在一个单独的文件中：

source("convdat.R")

运行所有可能的优化器：内置 NM 和 bobyqa；来自基础 R 的 nlminb 和 L-BFGS-B，通过optimx包；以及nloptrNM 和 bobyqa 的版本。

library(lme4)
g0.bobyqa <- glmer(resp ~ months.c * similarity * percSem +
                 (similarity | subj),
      family = binomial, data = myData,
                   control=glmerControl(optimizer="bobyqa"))
g0.NM <- update(g0.bobyqa,control=glmerControl(optimizer="Nelder_Mead"))
library(optimx)
g0.nlminb <- update(g0.bobyqa,control=glmerControl(optimizer="optimx",
                              optCtrl=list(method="nlminb")))
g0.LBFGSB <- update(g0.bobyqa,control=glmerControl(optimizer="optimx",
                              optCtrl=list(method="L-BFGS-B")))

library(nloptr)
## from https://github.com/lme4/lme4/issues/98:
defaultControl <- list(algorithm="NLOPT_LN_BOBYQA",xtol_rel=1e-6,maxeval=1e5)
nloptwrap2 <- function(fn,par,lower,upper,control=list(),...) {
    for (n in names(defaultControl)) 
      if (is.null(control[[n]])) control[[n]] <- defaultControl[[n]]
    res <- nloptr(x0=par,eval_f=fn,lb=lower,ub=upper,opts=control,...)
    with(res,list(par=solution,
                  fval=objective,
                  feval=iterations,
                  conv=if (status>0) 0 else status,
                  message=message))
}
g0.bobyqa2 <- update(g0.bobyqa,control=glmerControl(optimizer=nloptwrap2))
g0.NM2 <- update(g0.bobyqa,control=glmerControl(optimizer=nloptwrap2,
                           optCtrl=list(algorithm="NLOPT_LN_NELDERMEAD")))

总结结果。我们从 、 和 Nelder-Mead 收到警告nlminb（L-BFGS-B但最大 abs 梯度的大小在 Nelder-Mead 中最大）

getpar <- function(x) c(getME(x,c("theta")),fixef(x))
modList <- list(bobyqa=g0.bobyqa,NM=g0.NM,nlminb=g0.nlminb,
                bobyqa2=g0.bobyqa2,NM2=g0.NM2,LBFGSB=g0.LBFGSB)
ctab <- sapply(modList,getpar)
library(reshape2)
mtab <- melt(ctab)
library(ggplot2)
theme_set(theme_bw())
ggplot(mtab,aes(x=Var2,y=value,colour=Var2))+
    geom_point()+facet_wrap(~Var1,scale="free")

恰到好处的“好”：

ggplot(subset(mtab,Var2 %in% c("NM2","bobyqa","bobyqa2")),
       aes(x=Var2,y=value,colour=Var2))+
    geom_point()+facet_wrap(~Var1,scale="free")

优化器之间估计的变异系数：

summary(cvvec <- apply(ctab,1,function(x) sd(x)/mean(x)))

最高的 CV 是months.c, 仍然只有 4% 左右...

对数似然差异不大：NM2 给出了最大对数似然，所有“好”的都非常接近（即使是“坏”的也最多相差 1%）

likList <- sapply(modList,logLik)
round(log10(max(likList)-likList),1)
##  bobyqa      NM  nlminb bobyqa2     NM2  LBFGSB 
##    -8.5    -2.9    -2.0   -11.4    -Inf    -5.0
于 2014-01-26T22:19:27.647 回答