lme4 - warning messages when trying to run glmer in r

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Currently I'm trying to rerun an old data analysis, binomial glmer model, (from early 2013) on the latest version of R and lme4, because I don't have the old versions of R and lme4 anymore. However, I experience similar warning messages as previous threads by dmartin and carine (first warning message) and other threads outside stack overflow (warnings 2 and 3). These warning messages didn't pop up on the earlier version of R and lme4 I used, so it must have something to do with latest updates?
A subset of my dataset:
    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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    -2.96855001, 4.71144999, 4.71144999, 4.71144999, 4.71144999, 
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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, 
    190L, 190L, 190L, 190L, 190L, -160L, -160L, -160L, -160L, 
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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")
My script:
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$ValidDetections))
m1 <- glmer(Rdet ~ tm:Area + tm:c.distance + c.distance:Area + c.tm.depth:Area + c.receiver.depth:Area + c.temp:Area + c.wind:Area + c.tm.depth + c.receiver.depth + c.temp +c.wind + tm + c.distance + Area + replicate + (1|SUR.ID) + (1|Day) + (1|Unit) , data = df, family = binomial(link=logit))
(Unit = dispersion parameter used to calculate coefficients of determination)
In contrast to early 2013, the newest versions of R and lme4 return the following 3 warning messages:
1: In checkConv(attr(opt, "derivs"), opt$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?
I searched google and stack overflow for potential solutions to the above warning messages, however I cannot make sense out of them, and how it may be applied to my specific model / data.
Subsequently, I'm trying to find the MAM by using the drop1() function in R using a Chi^2 test and remove non-significant variables 1 at a time. Ignoring the above warning messages, I execute the following command:
drop1(m1,test="Chi")
However, this command cannot be used (i.e., returns addition warning messages) if the above warnings are not solved / dealt with first.
Does anyone know what is happening here? Please, can someone help me how to solve these warnings? Ignoring is not an option.
                can you try the version from Github (library(devtools); install_github("lme4","lme4") and see if this resolves your convergence warnings?
– Ben Bolker
                May 5, 2014 at 20:38
                @Ben, thank you for your reply. I just installed devtools and tried to install github. However, I get the following error messages: '* preparing 'lme4': * checking DESCRIPTION meta-information ... OK * cleaning src * installing the package to build vignettes * creating vignettes ... ERROR Error in texi2dvi(file = file, pdf = TRUE, clean = clean, quiet = quiet,  :    Running 'texi2dvi' on 'PLSvGLS.tex' failed. Calls: <Anonymous> -> texi2pdf -> texi2dvi Execution halted Error: Command failed (1)'
– FlyingDutch
                May 6, 2014 at 7:43
                not sure why it happens.  What OS are you on?  I can build a current binary version and post it at lme4.r-forge.r-project.org .  I have been working on your example; I still see some issues that I'm trying to work out.
– Ben Bolker
                May 6, 2014 at 14:14
                @BenBolker, I'm running on Mac OS X Mavericks. Thanks for your efforts, really appreciated.
– FlyingDutch
                May 6, 2014 at 14:48
                Apparently you can safely ignore these issues. Please see Ben Bolker's replies at stackoverflow.com/questions/21344555/… for more details.
– FlyingDutch
                May 23, 2014 at 18:51
tl;dr at least based on the subset of data you provided, this is a fairly unstable fit.  The warnings about near unidentifiability go away if you scale the continuous predictors.  Trying with a wide variety of optimizers, we get about the same log-likelihoods, and parameter estimates that vary by a few percent; two optimizers (nlminb from base R and BOBYQA from the nloptr package) converge without warnings, and are probably giving the "correct" answer.  I haven't computed confidence intervals, but I suspect that they're very wide.  (Your mileage may differ somewhat with your full data set ...)
source("SO_23478792_dat.R")  ## I put the data you provided in here
Basic fit (replicated from above):
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$ValidDetections))
m1 <- glmer(Rdet ~ tm:Area + tm:c.distance +
            c.distance:Area + c.tm.depth:Area +
            c.receiver.depth:Area + c.temp:Area +
            c.wind:Area +
            c.tm.depth + c.receiver.depth +
            c.temp +c.wind + tm + c.distance + Area +
            replicate +
            (1|SUR.ID) + (1|Day) + (1|Unit) ,
            data = df, family = binomial(link=logit))
I get more or less the same warnings you did, slightly fewer since the development version has been a little improved/tweaked:
## 1: In checkConv(attr(opt, "derivs"), opt$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$checkConv,  :
##   Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?
I tried various little things (restarting from the previous fitted values, switching optimizers) without much change in the results (i.e. the same warnings).
ss <- getME(m1,c("theta","fixef"))
m2 <- update(m1,start=ss,control=glmerControl(optCtrl=list(maxfun=2e4)))
m3 <- update(m1,start=ss,control=glmerControl(optimizer="bobyqa",
                         optCtrl=list(maxfun=2e4)))
Following the advice in the warning message (rescaling the continuous predictors):
numcols <- grep("^c\\.",names(df))
dfs <- df
dfs[,numcols] <- scale(dfs[,numcols])
m4 <- update(m1,data=dfs)
This gets rid of scaling warnings, but the warning about large gradients persists.
Use some utility code to fit the same model with many different optimizers:
afurl <- "https://raw.githubusercontent.com/lme4/lme4/master/misc/issues/allFit.R"
## http://tonybreyal.wordpress.com/2011/11/24/source_https-sourcing-an-r-script-from-github/
library(RCurl)
eval(parse(text=getURL(afurl)))
aa <- allFit(m4)
is.OK <- sapply(aa,is,"merMod")  ## nlopt NELDERMEAD failed, others succeeded
## extract just the successful ones
aa.OK <- aa[is.OK]
Pull out warnings:
lapply(aa.OK,function(x) x@optinfo$conv$lme4$messages)
(All but nlminb and nloptr BOBYQA give convergence warnings.)
Log-likelihoods are all approximately the same:
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 
(again, nlminb and nloptr BOBYQA have the best fits/highest log-likelihoods)
Compare fixed effect parameters across optimizers:
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 
Compare variance estimates (not as interesting: all optimizers except N-M give exactly
zero variance for Day and SUR.ID)
aa.varcorr <- t(sapply(aa.OK,function(x) unlist(VarCorr(x))))
aa.varcorr.m <- melt(aa.varcorr)
gplot1 %+% aa.varcorr.m
I tried to run this with lme4.0 ("old lme4"), but got various "Downdated VtV" errors, even with the scaled data set.  Perhaps that problem would go away with the full data set?
I haven't yet explored why drop1 doesn't work properly if the initial fit returns warnings ...
                Unfortunately the Downdated X'X errors(?)(under R OSX SL and higher) do not go away when using the older lme4 version on the full dataset. On a side note: I managed to install OSX Snow Leopard on another drive, using R v2.14 and v2.15 in combination with the old lme4 package. Strangely enough, that combination does work - does not produce errors. Also, I had my script ran on a windows pc, and no errors occurred. So my bet is that it has something to do with running from OSX Mavericks that causes these errors, regardless which R version you use (Snow Leopard or higher OR Mavericks or higher)
– FlyingDutch
                May 11, 2014 at 13:39
                Hmm.  Is there any chance you can send the data (to be shared only with me and one of my co-workers who has Mavericks installed)?
– Ben Bolker
                May 11, 2014 at 13:42
                On a side note: I tried to run old lme4 on R for mavericks and higher, and got returned the following error message: Loading required package: Matrix Error in dyn.load(file, DLLpath = DLLpath, ...):    unable to load shared object '/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): Library not loaded: /usr/local/lib/libgfortran.2.dylib   Referenced from: /Library/Frameworks/R.framework/[..]Reason: image not found Error: package or namespace load failed for ‘lme4.0’
– FlyingDutch
                May 11, 2014 at 13:44
                Hi @BenBolker; I am receiving similar messages on rescaling variables / large eigenvalue. Messages persist on rescaling & different optimisers. There is not a huge difference in fixed effects & log-likelihood between different optimiser models. Are the effects of these potentially problematic (I cant find anything about this). Thanks.
– user20650
                Oct 1, 2016 at 18:49
                if the answers are sufficiently similar across optimizers that none of your conclusions change, then I'd say you're OK.
– Ben Bolker
                Oct 1, 2016 at 18:56