The below fit generates convergence warnings. However, wrapping the model assigment in a summary function makes the warnings show in the top-level function, in this case the knitting routine. Hence, the convergence warnings are missing from the knit document and do not reproduce the output one would see in interactive usage faithfully.
library("lme4")
packageVersion("lme4")## [1] '1.1.7' 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,
-0.612192308, -0.612192308, -0.612192308, -0.612192308, -0.612192308,
-0.612192308, -0.597192308, -0.597192308, -0.597192308, -0.597192308,
-0.597192308, -0.597192308, -0.607192308, -0.607192308, -0.607192308,
-0.607192308, -0.607192308, -0.607192308, -0.327192308, -0.327192308,
-0.327192308, -0.327192308, -0.327192308, -0.327192308, -0.572192308,
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-0.272192308, -0.272192308, -0.272192308, -0.272192308, -0.547192308,
-0.547192308, -0.547192308, -0.547192308, -0.547192308, -0.547192308,
-0.607192308, -0.607192308, -0.607192308, -0.607192308, -0.607192308,
-0.607192308, 0.552807692, 0.552807692, 0.552807692, 0.552807692,
0.552807692, 0.552807692, 0.402807692, 0.402807692, 0.402807692,
0.402807692, 0.402807692, 0.402807692, 0.777807692, 0.777807692,
0.777807692, 0.777807692, 0.777807692, 0.777807692, 0.752807692,
0.752807692, 0.752807692, 0.752807692, 0.752807692, 0.752807692,
0.752807692, 0.752807692, 0.752807692, 0.752807692, 0.752807692,
0.752807692, 0.402807692, 0.402807692, 0.402807692, 0.402807692,
0.402807692, 0.402807692, 0.292807692, 0.292807692, 0.292807692,
0.292807692, 0.292807692, 0.292807692, 0.667807692, 0.667807692,
0.667807692, 0.667807692, 0.667807692, 0.667807692, 0.677807692,
0.677807692, 0.677807692, 0.677807692, 0.677807692, 0.677807692,
0.777807692, 0.777807692, 0.777807692, 0.777807692, 0.777807692,
0.777807692, 0.252807692, 0.252807692, 0.252807692, 0.252807692,
0.252807692, 0.252807692, 0.352807692, 0.352807692, 0.352807692,
0.352807692, 0.352807692, 0.352807692, 0.502807692, 0.502807692,
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,
-4.095807692, -4.095807692, -4.095807692, -4.095807692, -4.095807692,
-4.220807692, -4.220807692, -4.220807692, -4.220807692, -4.220807692,
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-3.820807692, -3.640807692, -3.640807692, -3.640807692, -3.640807692,
-3.640807692, -3.640807692, -3.660807692, -3.660807692, -3.660807692,
-3.660807692, -3.660807692, -3.660807692, -3.620807692, -3.620807692,
-3.620807692, -3.620807692, -3.620807692, -3.620807692, 0.074192308,
0.074192308, 0.074192308, 0.074192308, 0.074192308, 0.074192308,
-0.015807692, -0.015807692, -0.015807692, -0.015807692, -0.015807692,
-0.015807692, 0.324192308, 0.324192308, 0.324192308, 0.324192308,
0.324192308, 0.324192308, 0.544192308, 0.544192308, 0.544192308,
0.544192308, 0.544192308, 0.544192308, 0.759192308, 0.759192308,
0.759192308, 0.759192308, 0.759192308, 0.759192308, 1.324192308,
1.324192308, 1.324192308, 1.324192308, 1.324192308, 1.324192308,
1.549192308, 1.549192308, 1.549192308, 1.549192308, 1.549192308,
1.549192308, 1.709192308, 1.709192308, 1.709192308, 1.709192308,
1.709192308, 1.709192308, 1.639192308, 1.639192308, 1.639192308,
1.639192308, 1.639192308, 1.639192308, 1.579192308, 1.579192308,
1.579192308, 1.579192308, 1.579192308, 1.579192308, 2.724192308,
2.724192308, 2.724192308, 2.724192308, 2.724192308, 2.724192308,
2.839192308, 2.839192308, 2.839192308, 2.839192308, 2.839192308,
2.839192308, 1.064192308, 1.064192308, 1.064192308, 1.064192308,
1.064192308, 1.064192308, 1.184192308, 1.184192308, 1.184192308,
1.184192308, 1.184192308, 1.184192308, 1.254192308, 1.254192308,
1.254192308, 1.254192308, 1.254192308, 1.254192308, 1.379192308,
1.379192308, 1.379192308, 1.379192308, 1.379192308, 1.379192308,
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,
0.984192308, 0.984192308, 0.984192308, 0.984192308, 0.984192308,
0.984192308, -1.545807692, -1.545807692, -1.545807692, -1.545807692,
-1.545807692, -1.545807692, -1.475807692, -1.475807692, -1.475807692,
-1.475807692, -1.475807692, -1.475807692, -1.460807692, -1.460807692,
-1.460807692, -1.460807692, -1.460807692, -1.460807692, -1.340807692,
-1.340807692, -1.340807692, -1.340807692, -1.340807692, -1.340807692,
-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,
-2.96855001, -2.96855001, -2.96855001, -2.96855001, -2.96855001,
-2.96855001, -2.96855001, -2.96855001, -2.96855001, -2.96855001,
-2.96855001, -2.96855001, -2.96855001, -2.96855001, -2.96855001,
-2.96855001, -2.96855001, -2.96855001, -2.96855001, -2.96855001,
-2.96855001, -2.96855001, -2.96855001, -2.96855001, -2.96855001,
-2.96855001, -2.96855001, -2.96855001, -2.96855001, -2.96855001,
-2.96855001, -2.96855001, -2.96855001, -2.96855001, -2.96855001,
-2.96855001, -2.96855001, -2.96855001, -2.96855001, -2.96855001,
-2.96855001, -2.96855001, -2.96855001, -2.96855001, -2.96855001,
-2.96855001, -2.96855001, -2.96855001, -2.96855001, -2.96855001,
-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,
-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, -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")
df$SUR.ID <- factor(df$SUR.ID)
df$replicate <- factor(df$replicate)
Rdet <- cbind(df$ValidDetections,df$FalseDetections)
Unit <- factor(1:length(df$ValidDetections))
summary(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)))## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## 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
##
## AIC BIC logLik deviance df.resid
## 252 317 -107 214 201
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.119 0.000 0.000 0.348 1.294
##
## Random effects:
## Groups Name Variance Std.Dev.
## Unit (Intercept) 0.4639711 0.68115
## Day (Intercept) 0.0000807 0.00898
## SUR.ID (Intercept) 0.0000521 0.00722
## Number of obs: 220, groups: Unit, 220; Day, 5; SUR.ID, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -11.39192 7.15028 -1.59 0.11111
## c.tm.depth -1.02068 1.38940 -0.73 0.46257
## c.receiver.depth 6.81129 8.97835 0.76 0.44807
## c.temp -5.48720 2.79632 -1.96 0.04973 *
## c.wind -6.27121 3.71607 -1.69 0.09149 .
## tmPT-04 -2.14740 0.56683 -3.79 0.00015 ***
## c.distance -0.00428 0.00301 -1.42 0.15436
## AreaFinger 11.58507 7.26403 1.59 0.11074
## replicate2 2.69548 1.26037 2.14 0.03247 *
## tmPT-04:AreaFinger 0.45638 0.68964 0.66 0.50812
## tmPT-04:c.distance -0.00586 0.00367 -1.60 0.11021
## AreaFinger:c.distance 0.01307 0.00445 2.93 0.00334 **
## AreaFinger:c.tm.depth -3.04923 4.99494 -0.61 0.54155
## AreaFinger:c.receiver.depth -34.88318 17.00082 -2.05 0.04018 *
## AreaFinger:c.temp 2.19563 1.87003 1.17 0.24035
## AreaFinger:c.wind 8.35328 4.16364 2.01 0.04483 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) c.tm.d c.rcv. c.temp c.wind tmPT-04 c.dstn ArFngr
## c.tm.depth -0.006
## c.rcvr.dpth 0.207 -0.262
## c.temp 0.992 0.049 0.096
## c.wind 0.990 0.050 0.304 0.975
## tmPT-04 0.050 0.135 -0.185 0.106 0.059
## c.distance -0.487 -0.154 0.153 -0.517 -0.464 -0.056
## AreaFinger -0.982 0.006 -0.204 -0.974 -0.972 -0.049 0.476
## replicate2 -0.902 -0.001 0.051 -0.914 -0.855 -0.121 0.526 0.886
## tmPT-04:ArF -0.026 -0.032 0.080 -0.060 -0.036 -0.599 -0.177 0.021
## tmPT-04:c.d -0.006 0.148 -0.141 0.015 -0.012 0.423 -0.440 0.008
## ArFngr:c.ds -0.002 0.012 0.001 0.000 0.003 -0.268 -0.217 -0.048
## ArFngr:c.t. -0.494 -0.246 0.074 -0.511 -0.484 -0.013 0.258 0.599
## ArFngr:c.r. 0.340 0.132 -0.547 0.404 0.266 0.136 -0.319 -0.274
## ArFngr:c.tm -0.920 -0.072 -0.175 -0.925 -0.924 -0.084 0.452 0.925
## ArFngr:c.wn -0.958 -0.044 -0.268 -0.946 -0.963 -0.061 0.455 0.907
## rplct2 tPT-04:A tPT-04:. ArFngr:c.d ArFngr:c.t. ArFngr:c.r.
## c.tm.depth
## c.rcvr.dpth
## c.temp
## c.wind
## tmPT-04
## c.distance
## AreaFinger
## replicate2
## tmPT-04:ArF 0.044
## tmPT-04:c.d -0.068 0.159
## ArFngr:c.ds 0.058 -0.025 -0.336
## ArFngr:c.t. 0.534 0.161 0.117 -0.438
## ArFngr:c.r. -0.522 -0.082 0.056 0.030 -0.361
## ArFngr:c.tm 0.743 0.069 0.012 -0.069 0.455 -0.147
## ArFngr:c.wn 0.845 0.034 0.009 0.018 0.440 -0.452
## ArFngr:c.t
## c.tm.depth
## c.rcvr.dpth
## c.temp
## c.wind
## tmPT-04
## c.distance
## AreaFinger
## replicate2
## tmPT-04:ArF
## tmPT-04:c.d
## ArFngr:c.ds
## ArFngr:c.t.
## ArFngr:c.r.
## ArFngr:c.tm
## ArFngr:c.wn 0.838