предупреждающие сообщения при попытке запустить glmer в г
Уважаемое сообщество переполнения стека,
В настоящее время я пытаюсь перезапустить старый анализ данных, модель биномиального блеска (с начала 2013 года) на последних версиях R и lme4, потому что у меня больше нет старых версий R и lme4. Тем не менее, я получаю предупреждающие сообщения, похожие на предыдущие потоки от dmartin и carine (первое предупреждающее сообщение), и другие потоки вне переполнения стека (предупреждения 2 и 3). Эти предупреждающие сообщения не появлялись в более ранних версиях R и lme4, которые я использовал, так что это как-то связано с последними обновлениями?
Подмножество моего набора данных:
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,
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)), .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")
Мой сценарий:
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$ValidDetections))
m1 <- glmer(Rdet ~ tm:Area + tm:c.distance + c.distance:Area + c.tm.depth:Area + c.receiver.depth:Area + c.temp:Area + c.wind:Area + c.tm.depth + c.receiver.depth + c.temp +c.wind + tm + c.distance + Area + replicate + (1|SUR.ID) + (1|Day) + (1|Unit) , data = df, family = binomial(link=logit))
(Единица = параметр дисперсии, используемый для расчета коэффициентов определения)
В отличие от начала 2013 года, новейшие версии R и lme4 возвращают следующие 3 предупреждающих сообщения:
1: In checkConv(attr(opt, "derivs"), opt$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?
Я искал в Google и переполнении стека возможные решения вышеупомянутых предупреждающих сообщений, однако я не могу понять из них, и как это может быть применено к моей конкретной модели / данным.
Впоследствии я пытаюсь найти MAM, используя функцию drop1 () в R, используя тест Chi ^ 2, и удаляю незначимые переменные по 1 за раз. Игнорируя приведенные выше предупреждающие сообщения, я выполняю следующую команду:
drop1(m1,test="Chi")
Однако эта команда не может быть использована (то есть возвращает предупреждающие сообщения о добавлении), если вышеупомянутые предупреждения не были решены / обработаны в первую очередь.
Кто-нибудь знает, что здесь происходит? Пожалуйста, кто-нибудь может мне помочь, как решить эти предупреждения? Игнорирование не вариант.
Спасибо,
С наилучшими пожеланиями, Маврикий