尝试在 r 中运行 glmer 时出现警告消息

【问题标题】：warning messages when trying to run glmer in r尝试在 r 中运行 glmer 时出现警告消息
【发布时间】：2014-06-22 03:04:07
【问题描述】：

尊敬的 Stack Overflow 社区，

目前我正在尝试在最新版本的 R 和 lme4 上重新运行旧的数据分析、二项式 glmer 模型（从 2013 年初开始），因为我不再拥有旧版本的 R 和 lme4。但是，我遇到了与 dmartin 和 carine 之前的线程（第一个警告消息）和堆栈溢出之外的其他线程（警告 2 和 3）类似的警告消息。这些警告信息在我使用的早期版本的 R 和 lme4 上没有弹出，所以它必须与最新更新有关？

我的数据集的一个子集：

    df <- structure(list(SUR.ID = structure(c(1L, 1L, 2L, 2L, 3L, 3L, 1L, 
1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 
3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 
2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 
3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 
2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 
3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 
2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 
3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 
2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 
3L, 1L, 1L, 2L, 2L), .Label = c("10185", "10186", "10250"), class = "factor"), 
    tm = structure(c(1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 
    1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 
    2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
    1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
    2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L
    ), .Label = c("CT", "PT-04"), class = "factor"), ValidDetections = c(0L, 
    0L, 6L, 5L, 1L, 7L, 0L, 0L, 5L, 8L, 7L, 3L, 0L, 0L, 1L, 4L, 
    1L, 0L, 0L, 0L, 0L, 1L, 2L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 
    0L, 3L, 5L, 5L, 4L, 0L, 0L, 6L, 7L, 6L, 5L, 0L, 0L, 0L, 1L, 
    2L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 23L, 
    21L, 15L, 28L, 11L, 27L, 22L, 31L, 29L, 30L, 32L, 45L, 18L, 
    19L, 29L, 26L, 32L, 43L, 7L, 5L, 7L, 4L, 6L, 10L, 0L, 0L, 
    0L, 0L, 0L, 0L, 24L, 22L, 19L, 23L, 21L, 34L, 9L, 13L, 30L, 
    25L, 33L, 21L, 4L, 18L, 22L, 29L, 11L, 38L, 2L, 7L, 5L, 7L, 
    6L, 9L, 0L, 0L, 0L, 0L, 0L, 0L, 23L, 20L, 24L, 26L, 29L, 
    34L, 6L, 7L, 5L, 4L, 6L, 10L, 0L, 0L, 3L, 0L, 1L, 6L, 0L, 
    0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 2L, 0L, 5L, 0L, 0L, 0L, 0L, 
    0L, 1L, 0L, 0L, 0L, 3L, 1L, 11L, 0L, 0L, 2L, 5L, 1L, 2L, 
    0L, 0L, 0L, 3L, 0L, 4L, 0L, 0L, 0L, 2L, 0L, 2L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 4L, 2L, 5L, 6L, 6L, 2L, 3L, 0L, 0L, 1L, 
    3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 21L, 12L, 
    15L, 8L, 23L, 7L, 2L, 2L, 1L, 1L), CountDetections = c(0L, 
    0L, 7L, 5L, 3L, 7L, 0L, 0L, 5L, 8L, 8L, 4L, 0L, 0L, 1L, 4L, 
    1L, 1L, 0L, 0L, 0L, 1L, 3L, 3L, 0L, 0L, 1L, 0L, 2L, 4L, 0L, 
    0L, 4L, 5L, 5L, 5L, 0L, 0L, 6L, 7L, 7L, 5L, 0L, 0L, 0L, 1L, 
    2L, 2L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 2L, 23L, 
    21L, 18L, 28L, 11L, 27L, 23L, 31L, 29L, 30L, 34L, 45L, 19L, 
    19L, 29L, 26L, 32L, 43L, 7L, 5L, 7L, 4L, 6L, 10L, 0L, 0L, 
    0L, 0L, 0L, 0L, 24L, 22L, 19L, 23L, 21L, 34L, 10L, 15L, 30L, 
    25L, 34L, 24L, 4L, 19L, 23L, 29L, 13L, 38L, 2L, 7L, 5L, 7L, 
    7L, 9L, 0L, 0L, 0L, 0L, 0L, 0L, 23L, 20L, 24L, 26L, 29L, 
    34L, 6L, 7L, 5L, 4L, 6L, 10L, 0L, 0L, 4L, 1L, 1L, 7L, 0L, 
    0L, 0L, 3L, 2L, 1L, 0L, 0L, 0L, 3L, 0L, 5L, 0L, 0L, 2L, 2L, 
    0L, 1L, 0L, 0L, 0L, 5L, 1L, 11L, 0L, 0L, 3L, 5L, 1L, 2L, 
    0L, 0L, 2L, 3L, 0L, 6L, 0L, 0L, 0L, 3L, 0L, 3L, 0L, 0L, 1L, 
    0L, 0L, 1L, 0L, 0L, 6L, 2L, 5L, 6L, 7L, 4L, 5L, 1L, 0L, 3L, 
    3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 23L, 12L, 
    16L, 10L, 23L, 10L, 2L, 2L, 1L, 1L), FalseDetections = c(0L, 
    0L, 1L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 
    0L, 1L, 0L, 0L, 0L, 0L, 1L, 2L, 0L, 0L, 1L, 0L, 0L, 4L, 0L, 
    0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
    0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 
    0L, 3L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 1L, 2L, 0L, 0L, 1L, 3L, 0L, 1L, 1L, 0L, 
    2L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 
    0L, 1L, 0L, 0L, 0L, 2L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
    0L, 2L, 2L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 1L, 0L, 
    0L, 0L, 0L, 0L, 2L, 0L, 0L, 2L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 
    0L, 1L, 0L, 0L, 1L, 0L, 0L, 2L, 0L, 0L, 0L, 1L, 2L, 2L, 1L, 
    0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 
    0L, 1L, 2L, 0L, 3L, 0L, 0L, 0L, 0L), replicate = structure(c(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, 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, 2L, 2L, 2L, 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, 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, 2L, 2L, 2L, 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, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 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), .Label = c("1", "2"), class = "factor"), 
    Area = structure(c(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, 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, 1L, 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, 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, 2L, 2L, 
    2L, 2L, 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, 
    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, 1L, 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, 2L
    ), .Label = c("Drug Channel", "Finger"), class = "factor"), 
    Day = structure(c(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, 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, 1L, 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, 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, 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, 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, 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, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L
    ), .Label = c("03/06/13", "2/22/13", "2/26/13", "2/27/13", 
    "3/14/13"), class = "factor"), R.det = c(0, 0, 0.857142857, 
    1, 0.333333333, 1, 0, 0, 1, 1, 0.875, 0.75, 0, 0, 1, 1, 1, 
    0, 0, 0, 0, 1, 0.666666667, 0.333333333, 0, 0, 0, 0, 1, 0, 
    0, 0, 0.75, 1, 1, 0.8, 0, 0, 1, 1, 0.857142857, 1, 0, 0, 
    0, 1, 1, 0.5, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0.833333333, 
    1, 1, 1, 0.956521739, 1, 1, 1, 0.941176471, 1, 0.947368421, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 
    1, 1, 1, 1, 0.9, 0.866666667, 1, 1, 0.970588235, 0.875, 1, 
    0.947368421, 0.956521739, 1, 0.846153846, 1, 1, 1, 1, 1, 
    0.857142857, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 0, 0, 0.75, 0, 1, 0.857142857, 0, 0, 0, 0.333333333, 
    0.5, 1, 0, 0, 0, 0.666666667, 0, 1, 0, 0, 0, 0, 0, 1, 0, 
    0, 0, 0.6, 1, 1, 0, 0, 0.666666667, 1, 1, 1, 0, 0, 0, 1, 
    0, 0.666666667, 0, 0, 0, 0.666666667, 0, 0.666666667, 0, 
    0, 0, 0, 0, 0, 0, 0, 0.666666667, 1, 1, 1, 0.857142857, 0.5, 
    0.6, 0, 0, 0.333333333, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0.913043478, 1, 0.9375, 0.8, 1, 0.7, 1, 1, 1, 1), c.receiver.depth = c(-0.2, 
    -0.2, -0.2, -0.2, -0.2, -0.2, -0.22, -0.22, -0.22, -0.22, 
    -0.22, -0.22, -0.22, -0.22, -0.22, -0.22, -0.22, -0.22, -0.225, 
    -0.225, -0.225, -0.225, -0.225, -0.225, -0.225, -0.225, -0.225, 
    -0.225, -0.225, -0.225, -0.205, -0.205, -0.205, -0.205, -0.205, 
    -0.205, -0.185, -0.185, -0.185, -0.185, -0.185, -0.185, -0.18, 
    -0.18, -0.18, -0.18, -0.18, -0.18, -0.165, -0.165, -0.165, 
    -0.165, -0.165, -0.165, -0.14, -0.14, -0.14, -0.14, -0.14, 
    -0.14, -0.34, -0.34, -0.34, -0.34, -0.34, -0.34, -0.365, 
    -0.365, -0.365, -0.365, -0.365, -0.365, -0.365, -0.365, -0.365, 
    -0.365, -0.365, -0.365, -0.38, -0.38, -0.38, -0.38, -0.38, 
    -0.38, -0.385, -0.385, -0.385, -0.385, -0.385, -0.385, -0.395, 
    -0.395, -0.395, -0.395, -0.395, -0.395, -0.4, -0.4, -0.4, 
    -0.4, -0.4, -0.4, -0.395, -0.395, -0.395, -0.395, -0.395, 
    -0.395, -0.38, -0.38, -0.38, -0.38, -0.38, -0.38, -0.37, 
    -0.37, -0.37, -0.37, -0.37, -0.37, -0.285, -0.285, -0.285, 
    -0.285, -0.285, -0.285, -0.31, -0.31, -0.31, -0.31, -0.31, 
    -0.31, 0.22, 0.22, 0.22, 0.22, 0.22, 0.22, 0.225, 0.225, 
    0.225, 0.225, 0.225, 0.225, 0.225, 0.225, 0.225, 0.225, 0.225, 
    0.225, 0.21, 0.21, 0.21, 0.21, 0.21, 0.21, 0.185, 0.185, 
    0.185, 0.185, 0.185, 0.185, 0.175, 0.175, 0.175, 0.175, 0.175, 
    0.175, 0.14, 0.14, 0.14, 0.14, 0.14, 0.14, 0.13, 0.13, 0.13, 
    0.13, 0.13, 0.13, 0.105, 0.105, 0.105, 0.105, 0.105, 0.105, 
    0.215, 0.215, 0.215, 0.215, 0.215, 0.215, 0.54, 0.54, 0.54, 
    0.54, 0.54, 0.54, 0.525, 0.525, 0.525, 0.525, 0.525, 0.525, 
    0.515, 0.515, 0.515, 0.515, 0.515, 0.515, 0.545, 0.545, 0.545, 
    0.545, 0.545, 0.545, 0.525, 0.525, 0.525, 0.525), c.tm.depth = c(0.042807692, 
    0.042807692, 0.042807692, 0.042807692, 0.042807692, 0.042807692, 
    -0.282192308, -0.282192308, -0.282192308, -0.282192308, -0.282192308, 
    -0.282192308, -0.427192308, -0.427192308, -0.427192308, -0.427192308, 
    -0.427192308, -0.427192308, -0.027192308, -0.027192308, -0.027192308, 
    -0.027192308, -0.027192308, -0.027192308, 0.022807692, 0.022807692, 
    0.022807692, 0.022807692, 0.022807692, 0.022807692, 0.042807692, 
    0.042807692, 0.042807692, 0.042807692, 0.042807692, 0.042807692, 
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    -1.265807692, -1.265807692, -1.265807692, -1.265807692), 
    c.wind = c(1.27535159, 1.27535159, 1.27535159, 1.27535159, 
    1.27535159, 1.27535159, 1.27535159, 1.27535159, 1.27535159, 
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    5.88092439, 5.88092439, 5.88092439, 5.88092439, 5.88092439, 
    5.88092439), c.distance = c(-160L, -160L, -160L, -160L, -160L, 
    -160L, -110L, -110L, -110L, -110L, -110L, -110L, -10L, -10L, 
    -10L, -10L, -10L, -10L, 90L, 90L, 90L, 90L, 90L, 90L, 190L, 
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    190L, 190L, 190L, 190L, -160L, -160L, -160L, -160L, -160L, 
    -160L, -110L, -110L, -110L, -110L, -110L, -110L, -10L, -10L, 
    -10L, -10L, -10L, -10L, 90L, 90L, 90L, 90L, 90L, 90L, 190L, 
    190L, 190L, 190L, 190L, 190L, -160L, -160L, -160L, -160L, 
    -160L, -160L, -10L, -10L, -10L, -10L, -10L, -10L, 90L, 90L, 
    90L, 90L, 90L, 90L, 190L, 190L, 190L, 190L, 190L, 190L, -160L, 
    -160L, -160L, -160L, -160L, -160L, -110L, -110L, -110L, -110L
    )), .Names = c("SUR.ID", "tm", "ValidDetections", "CountDetections", 
"FalseDetections", "replicate", "Area", "Day", "R.det", "c.receiver.depth", 
"c.tm.depth", "c.temp", "c.wind", "c.distance"), row.names = c(NA, 
-220L), class = "data.frame")

我的脚本：

library(lme4)
df$SUR.ID <- factor(df$SUR.ID)
df$replicate <- factor(df$replicate)
Rdet <- cbind(df$ValidDetections,df$FalseDetections)
Unit <- factor(1:length(df$ValidDetections))
m1 <- glmer(Rdet ~ tm:Area + tm:c.distance + c.distance:Area + c.tm.depth:Area + c.receiver.depth:Area + c.temp:Area + c.wind:Area + c.tm.depth + c.receiver.depth + c.temp +c.wind + tm + c.distance + Area + replicate + (1|SUR.ID) + (1|Day) + (1|Unit) , data = df, family = binomial(link=logit))

（单位 = 用于计算决定系数的分散参数）

与 2013 年初相比，最新版本的 R 和 lme4 返回以下 3 条警告消息：

1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model failed to converge with max|grad| = 62.5817 (tol = 0.001)
2: In if (resHess$code != 0) { :
  the condition has length > 1 and only the first element will be used
3: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
 - Rescale variables?

我在谷歌和堆栈溢出中搜索了上述警告消息的潜在解决方案，但我无法理解它们，以及它如何应用于我的特定模型/数据。

随后，我尝试使用 R 中的 drop1() 函数使用 Chi^2 测试来查找 MAM，并一次删除非重要变量 1。忽略上述警告信息，我执行以下命令：

drop1(m1,test="Chi")

但是，如果没有首先解决/处理上述警告，则无法使用此命令（即返回附加警告消息）。

有人知道这里发生了什么吗？拜托，有人可以帮我解决这些警告吗？忽略不是一种选择。

非常感谢，

最好的祝愿， 莫里茨

【问题讨论】：

  • 你可以试试 Github 的版本 (library(devtools); install_github("lme4","lme4") 看看这是否解决了你的收敛警告？
  • @Ben，谢谢你的回复。我刚刚安装了 devtools 并尝试安装 github。但是，我收到以下错误消息： '* 准备 'lme4': * 检查说明元信息 ... OK * 清理 src * 安装包以构建小插图 * 创建小插图 ... 错误 texi2dvi 中的错误（文件 = 文件, pdf = TRUE, clean = clean, quiet = quiet, : Running 'texi2dvi' on 'PLSvGLS.tex' failed. Calls: -> texi2pdf -> texi2dvi Execution halted Error: Command failed (1)'
  • 不知道为什么会这样。你在什么操作系统上？我可以构建一个当前的二进制版本并将其发布到 lme4.r-forge.r-project.org 。我一直在研究你的例子；我仍然看到一些我正在努力解决的问题。
  • @BenBolker，我在 Mac OS X Mavericks 上运行。感谢您的努力，非常感谢。
  • 显然您可以放心地忽略这些问题。有关详细信息，请参阅 Ben Bolker 在stackoverflow.com/questions/21344555/… 的回复。

标签： r lme4


【解决方案1】：

tl;dr 至少根据您提供的数据子集，这是一个相当不稳定的拟合。如果您缩放连续预测变量，关于接近不可识别性的警告就会消失。尝试使用各种优化器，我们得到大致相同的对数似然，参数估计值相差几个百分点；两个优化器（来自基本 R 的nlminb 和来自nloptr 包的 BOBYQA）在没有警告的情况下收敛，并且可能给出了“正确”的答案。我没有计算置信区间，但我怀疑它们非常宽。 （您的里程可能与您的完整数据集有所不同......）

source("SO_23478792_dat.R")  ## I put the data you provided in here

基本拟合（从上面复制）：

library(lme4)
df$SUR.ID <- factor(df$SUR.ID)
df$replicate <- factor(df$replicate)
Rdet <- cbind(df$ValidDetections,df$FalseDetections)
Unit <- factor(1:length(df$ValidDetections))
m1 <- glmer(Rdet ~ tm:Area + tm:c.distance +
            c.distance:Area + c.tm.depth:Area +
            c.receiver.depth:Area + c.temp:Area +
            c.wind:Area +
            c.tm.depth + c.receiver.depth +
            c.temp +c.wind + tm + c.distance + Area +
            replicate +
            (1|SUR.ID) + (1|Day) + (1|Unit) ,
            data = df, family = binomial(link=logit))

我收到的警告或多或少与您的警告相同，但由于开发版本进行了一些改进/调整，因此稍微少了一点：

## 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
##   Model failed to converge with max|grad| = 1.52673 (tol = 0.001, component 1)
## 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
##   Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?

我尝试了各种小事情（从以前的拟合值重新开始，切换优化器），结果没有太大变化（即相同的警告）。

ss <- getME(m1,c("theta","fixef"))
m2 <- update(m1,start=ss,control=glmerControl(optCtrl=list(maxfun=2e4)))
m3 <- update(m1,start=ss,control=glmerControl(optimizer="bobyqa",
                         optCtrl=list(maxfun=2e4)))

遵循警告消息中的建议（重新调整连续预测变量）：

numcols <- grep("^c\\.",names(df))
dfs <- df
dfs[,numcols] <- scale(dfs[,numcols])
m4 <- update(m1,data=dfs)

这消除了缩放警告，但关于大梯度的警告仍然存在。

使用一些实用程序代码来拟合具有许多不同优化器的相同模型：

afurl <- "https://raw.githubusercontent.com/lme4/lme4/master/misc/issues/allFit.R"
## http://tonybreyal.wordpress.com/2011/11/24/source_https-sourcing-an-r-script-from-github/
library(RCurl)
eval(parse(text=getURL(afurl)))
aa <- allFit(m4)
is.OK <- sapply(aa,is,"merMod")  ## nlopt NELDERMEAD failed, others succeeded
## extract just the successful ones
aa.OK <- aa[is.OK]

拉出警告：

lapply(aa.OK,function(x) x@optinfo$conv$lme4$messages)

（除了nlminb 和 nloptr BOBYQA 之外的所有都给出收敛警告。）

对数似然都大致相同：

summary(sapply(aa.OK,logLik),digits=6)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -107.127 -107.114 -107.111 -107.114 -107.110 -107.110

（同样，nlminb 和 nloptr BOBYQA 具有最佳拟合/最高对数似然）

比较优化器的固定效果参数：

aa.fixef <- t(sapply(aa.OK,fixef))
library(ggplot2)
library(reshape2)
library(plyr)
aa.fixef.m <- melt(aa.fixef)
models <- levels(aa.fixef.m$Var1)
(gplot1 <- ggplot(aa.fixef.m,aes(x=value,y=Var1,colour=Var1))+geom_point()+
    facet_wrap(~Var2,scale="free")+
    scale_y_discrete(breaks=models,
                     labels=abbreviate(models,6)))
## coefficients of variation of fixed-effect parameter estimates:
summary(unlist(daply(aa.fixef.m,"Var2",summarise,sd(value)/abs(mean(value)))))
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## 0.003573 0.013300 0.022730 0.019710 0.026200 0.035810

比较方差估计（没那么有趣：除了 N-M 之外的所有优化器都给出了准确的 Day 和 SUR.ID 的方差为零）

aa.varcorr <- t(sapply(aa.OK,function(x) unlist(VarCorr(x))))
aa.varcorr.m <- melt(aa.varcorr)
gplot1 %+% aa.varcorr.m

我尝试使用lme4.0 ("old lme4") 运行此程序，但出现各种“Downdated VtV”错误，即使使用缩放数据集也是如此。也许这个问题会随着完整的数据集而消失？

我还没有探究为什么如果初始拟合返回警告，drop1 不能正常工作...

【讨论】：

  • 不幸的是，在完整数据集上使用较旧的 lme4 版本时，Downdated X'X 错误（？）（在 R OSX SL 和更高版本下）不会消失。附带说明：我设法在另一个驱动器上安装了 OSX Snow Leopard，将 R v2.14 和 v2.15 与旧的 lme4 包结合使用。奇怪的是，这种组合确实有效——不会产生错误。另外，我的脚本在 Windows pc 上运行，没有发生错误。所以我敢打赌，这与从 OSX Mavericks 运行导致这些错误有关，无论您使用哪个 R 版本（Snow Leopard 或更高版本或 Mavericks 或更高版本）
  • 嗯。您是否有机会发送数据（仅与我和我的一位安装了 Mavericks 的同事共享）？
  • 附带说明：我尝试在 R 上为小牛和更高版本运行旧 lme4，并返回以下错误消息：Loading required package: Matrix Error in dyn.load(file, DLLpath = DLLpath , ...): 无法加载共享对象'/Library/Frameworks/R.framework/Versions/3.1/Resources/library/lme4.0/libs/lme4.so': dlopen(/Library/Frameworks/R.framework /Versions/3.1/Resources/library/lme4.0/libs/lme4.so, 6)：库未加载：/usr/local/lib/libgfortran.2.dylib 引用自：/Library/Frameworks/R.framework/ [..]原因：找不到图像错误：“lme4.0”的包或命名空间加载失败
  • 嗨@BenBolker；我收到关于重新调整变量/大特征值的类似消息。消息在重新缩放和不同的优化器上持续存在。不同优化器模型之间的固定效应和对数似然没有巨大差异。这些潜在问题的影响（我找不到任何关于此的内容）。谢谢。
  • 如果优化器的答案非常相似，以至于您的结论都没有改变，那么我会说您没问题。
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