rでglmerを実行しようとしたときの警告メッセージ
Stack Overflowコミュニティの皆様
現在、Rとlme4の古いバージョンはもうないので、Rとlme4の最新バージョンで（2013年の初めから）古いデータ分析の二項glmerモデルを再実行しようとしています。ただし、dmartinとcarineによる以前のスレッド（最初の警告メッセージ）やスタックオーバーフロー以外の他のスレッド（警告2および3）と同様の警告メッセージが発生します。これらの警告メッセージは、私が使用したRとlme4の以前のバージョンではポップアップ表示されなかったので、最新の更新と関係があるのでしょうか？
私のデータセットのサブセット：
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,
-0.312192308, -0.312192308, 0.062807692, 0.062807692, 0.062807692,
0.062807692, 0.062807692, 0.062807692, 0.127807692, 0.127807692,
0.127807692, 0.127807692, 0.127807692, 0.127807692, -0.592192308,
-0.592192308, -0.592192308, -0.592192308, -0.592192308, -0.592192308,
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0.502807692, 0.502807692, 0.502807692, 0.502807692, 0.027807692,
0.027807692, 0.027807692, 0.027807692, 0.027807692, 0.027807692,
0.077807692, 0.077807692, 0.077807692, 0.077807692), c.temp = c(-4.095807692,
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1.529192308, 1.529192308, 1.529192308, 1.529192308, 1.529192308,
1.529192308, 1.599192308, 1.599192308, 1.599192308, 1.599192308,
1.599192308, 1.599192308, 1.669192308, 1.669192308, 1.669192308,
1.669192308, 1.669192308, 1.669192308, 1.664192308, 1.664192308,
1.664192308, 1.664192308, 1.664192308, 1.664192308, 1.714192308,
1.714192308, 1.714192308, 1.714192308, 1.714192308, 1.714192308,
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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,
1.27535159, 1.27535159, 1.27535159, 1.27535159, 1.27535159,
1.27535159, 1.27535159, 1.27535159, 1.27535159, 1.27535159,
1.27535159, 1.27535159, 1.27535159, 1.27535159, 1.27535159,
1.27535159, 1.27535159, 1.27535159, 1.27535159, 1.27535159,
1.27535159, 1.27535159, 1.27535159, 1.27535159, 1.27535159,
1.27535159, 1.27535159, 1.27535159, 1.27535159, 1.27535159,
1.27535159, 1.27535159, 1.27535159, 1.27535159, 1.27535159,
1.27535159, 1.27535159, 1.27535159, 1.27535159, 1.27535159,
1.27535159, 1.27535159, 1.27535159, 1.27535159, 1.27535159,
1.27535159, 1.27535159, 1.27535159, 1.27535159, 1.27535159,
1.27535159, -2.96855001, -2.96855001, -2.96855001, -2.96855001,
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-2.96855001, -2.96855001, -2.96855001, -2.96855001, -2.96855001,
-2.96855001, 4.71144999, 4.71144999, 4.71144999, 4.71144999,
4.71144999, 4.71144999, 4.71144999, 4.71144999, 4.71144999,
4.71144999, 4.71144999, 4.71144999, -2.939182972, -2.939182972,
-2.939182972, -2.939182972, -2.939182972, -2.939182972, -2.939182972,
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-2.939182972, -2.939182972, -2.939182972, -2.939182972, -2.939182972,
-2.939182972, -2.939182972, -2.939182972, -2.939182972, -2.939182972,
-2.939182972, -2.939182972, -2.939182972, -2.939182972, -2.939182972,
-2.939182972, -2.939182972, -2.939182972, -2.939182972, -2.939182972,
-2.939182972, -2.939182972, -2.939182972, -2.939182972, -2.939182972,
-2.939182972, -2.939182972, -2.939182972, -2.939182972, -2.939182972,
-2.939182972, -2.939182972, -2.939182972, -2.939182972, -2.939182972,
-2.939182972, -2.939182972, -2.939182972, 5.88092439, 5.88092439,
5.88092439, 5.88092439, 5.88092439, 5.88092439, 5.88092439,
5.88092439, 5.88092439, 5.88092439, 5.88092439, 5.88092439,
5.88092439, 5.88092439, 5.88092439, 5.88092439, 5.88092439,
5.88092439, 5.88092439, 5.88092439, 5.88092439, 5.88092439,
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,
190L, 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, -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, -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, -110L, -110L, -110L, -110L, -110L, -110L,
-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, -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")
私のスクリプト：
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$ValidDetections))
m1 <- glmer(Rdet ~ tm:Area + tm:c.distance + c.distance:Area + c.tm.depth:Area + c.receiver.depth:Area + c.temp:Area + c.wind:Area + c.tm.depth + c.receiver.depth + c.temp +c.wind + tm + c.distance + Area + replicate + (1|SUR.ID) + (1|Day) + (1|Unit) , data = df, family = binomial(link=logit))
（単位=決定係数の計算に使用される分散パラメーター）
2013年の初めとは対照的に、Rとlme4の最新バージョンは、次の3つの警告メッセージを返します。
1: In checkConv(attr(opt, "derivs"), opt$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?
上記の警告メッセージの潜在的な解決策を探すためにグーグルとスタックオーバーフローを検索しましたが、それらを理解できず、特定のモデル/データにどのように適用できるかわかりません。
続いて、Chi ^ 2テストを使用してRでdrop1（）関数を使用し、重要でない変数を1つずつ削除して、MAMを見つけようとしています。上記の警告メッセージを無視して、次のコマンドを実行します。
drop1(m1,test="Chi")
ただし、上記の警告が最初に解決または処理されない場合、このコマンドは使用できません（つまり、追加の警告メッセージが返されます）。
ここで何が起こっているのか誰か知っていますか？これらの警告を解決する方法を誰かが手伝ってくれませんか？無視はオプションではありません。
本当にありがとう、
最高の願い、モーリッツ
tl; dr少なくとも提供したデータのサブセットに基づいて、これはかなり不安定な適合です。連続予測子をスケーリングすると、識別不能に近いという警告が消えます。さまざまなオプティマイザを試してみると、ほぼ同じ対数尤度が得られ、パラメータの推定値は数パーセント異なります。 2つのオプティマイザー（ベースRのnlminbとnloptrパッケージのBOBYQA）は警告なしで収束し、おそらく「正しい」答えを出しています。信頼区間は計算していませんが、非常に広いと思います。 （あなたの走行距離はあなたの完全なデータセットで多少異なるかもしれません...）
source("SO_23478792_dat.R") ## I put the data you provided in here
基本的なフィット（上から複製）：
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$ValidDetections))
m1 <- glmer(Rdet ~ tm:Area + tm:c.distance +
c.distance:Area + c.tm.depth:Area +
c.receiver.depth:Area + c.temp:Area +
c.wind:Area +
c.tm.depth + c.receiver.depth +
c.temp +c.wind + tm + c.distance + Area +
replicate +
(1|SUR.ID) + (1|Day) + (1|Unit) ,
data = df, family = binomial(link=logit))
私はあなたがしたのと同じ警告を多かれ少なかれ受け取ります、開発バージョンが少し改善/調整されたので少し少なくなりました：
## 1: In checkConv(attr(opt, "derivs"), opt$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$checkConv, :
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?
結果に大きな変更を加えずに（つまり、同じ警告）、さまざまな小さなことを試みました（以前の近似値から始めて、オプティマイザの切り替え）。
ss <- getME(m1,c("theta","fixef"))
m2 <- update(m1,start=ss,control=glmerControl(optCtrl=list(maxfun=2e4)))
m3 <- update(m1,start=ss,control=glmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e4)))
警告メッセージのアドバイスに従う（連続予測子の再スケーリング）：
numcols <- grep("^c\\.",names(df))
dfs <- df
dfs[,numcols] <- scale(dfs[,numcols])
m4 <- update(m1,data=dfs)
これにより、スケーリングの警告は取り除かれますが、大きな勾配に関する警告が引き続き表示されます。
いくつかのユーティリティコードを使用して、同じモデルを多くの異なるオプティマイザに適合させます。
afurl <- "https://raw.githubusercontent.com/lme4/lme4/master/misc/issues/allFit.R"
## http://tonybreyal.wordpress.com/2011/11/24/source_https-sourcing-an-r-script-from-github/
library(RCurl)
eval(parse(text=getURL(afurl)))
aa <- allFit(m4)
is.OK <- sapply(aa,is,"merMod") ## nlopt NELDERMEAD failed, others succeeded
## extract just the successful ones
aa.OK <- aa[is.OK]
警告を引き出す：
lapply(aa.OK,function(x) x@optinfo$conv$lme4$messages)
（nlminbおよびnloptr BOBYQAを除くすべてが収束の警告を出します。）
対数尤度はすべてほぼ同じです。
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
（ここでも、nlminbとnloptr BOBYQAが最もよく適合します/対数尤度が最も高くなります）
オプティマイザー間で固定効果パラメーターを比較します。
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
分散推定値を比較します（それほど興味深いものではありません：N-Mを除くすべてのオプティマイザーは、DayとSUR.IDの分散を正確にゼロにします）。
aa.varcorr <- t(sapply(aa.OK,function(x) unlist(VarCorr(x))))
aa.varcorr.m <- melt(aa.varcorr)
gplot1 %+% aa.varcorr.m
これをlme4.0（ "old lme4"）で実行しようとしましたが、スケーリングされたデータセットでも、さまざまな「Downdated VtV」エラーが発生しました。おそらく、その問題は完全なデータセットで解消されますか？
最初の近似で警告が返された場合にdrop1が正しく機能しない理由についてはまだ調べていません...