尝试在r中运行glmer时发出警告消息-Java 学习之路

亲爱的Stack Overflow社区，

目前我正试图在最新版本的R和lme4上重新运行旧数据分析，二项式glmer模型（从2013年初开始），因为我不再使用旧版本的R和lme4了 . 但是，我通过dmartin和carine（第一个警告消息）以及堆栈溢出之外的其他线程（警告2和3）经历与先前线程类似的警告消息 . 我使用的早期版本的R和lme4上没有弹出这些警告消息，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, 
    -0.267192308, -0.267192308, -0.267192308, -0.267192308, -0.267192308, 
    -0.267192308, -0.312192308, -0.312192308, -0.312192308, -0.312192308, 
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    -160L, -110L, -110L, -110L, -110L, -110L, -110L, -10L, -10L, 
    -10L, -10L, -10L, -10L, 90L, 90L, 90L, 90L, 90L, 90L, 190L, 
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    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")

我的剧本：

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=用于计算确定系数的分散参数）

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?

我搜索谷歌和堆栈溢出以寻找上述警告消息的潜在解决方案，但是我无法理解它们，以及它如何应用于我的特定模型/数据 .

随后，我试图通过使用Chi ^ 2测试中的drop1（）函数找到MAM，并一次删除1个非重要变量 . 忽略上述警告消息，我执行以下命令：

drop1(m1,test="Chi")

但是，如果不首先解决/处理上述警告，则不能使用该命令（即，返回添加警告消息） .

有谁知道这里发生了什么？请问，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，

最好的祝福，毛里求斯

1 回答

tl;dr 至少基于您提供的数据子集，这是一个相当不稳定的契合 . 如果您扩展连续预测因子，那么关于近乎不可识别性的警告就会消失 . 尝试使用各种各样的优化器，我们得到相同的对数似然，并且参数估计变化几个百分点;两个优化器（来自基础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"correct"答案 . 我很宽敞 . （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))

我得到了或多或少相同的警告，因为开发版本稍微改进/调整后略少一些：

## 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（从先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使相同的模型适合许多不同的优化器：

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之外的所有人都会发出收敛警告 . ）

对数似然性大致相同：

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

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

比较优化器中的固定效果参数：

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（不是很有趣：除了N-M之外的所有优化器都为Day和SUR.ID提供了完全零方差）

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"）运行它，但是即使使用缩放数据集也会出现各种"Downdated VtV"错误 . 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 drop1 如果初始拟合返回警告就无法正常工作......

回复于 2024-04-29T16:01:30+08:00

尝试在r中运行glmer时发出警告消息

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