Multinomial mixed effect model in R

Question

I would like to make a multinomial model with random effects, but I don't know how.

The model would look like this: native_driftertype ~treat+(1|replica)+(1|compartment/originhive),
with native_driftertype a factor with 5 levels, treat a factor with 3 levels, replica a factor with 2 levels, compartment a factor with 3 levels, and originhive a factor with 24 levels.

The data looks like this:

data6 <- structure(list(origin_hive = structure(c(1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 21L, 21L, 
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 23L, 23L, 23L, 23L, 23L, 
23L, 23L, 23L, 23L, 23L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 14L, 14L, 14L, 14L, 
14L, 14L, 14L, 14L, 14L, 14L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
19L, 19L, 19L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
17L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 18L, 18L, 
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
22L, 22L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 15L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 
24L, 3L, 3L, 19L, 3L, 3L, 19L, 3L, 3L, 3L, 3L, 19L, 19L, 19L, 
3L, 3L, 3L, 6L, 9L, 6L, 6L, 6L, 6L, 9L, 6L, 9L, 9L, 6L, 6L, 6L, 
9L, 6L, 8L, 16L, 8L, 16L, 16L, 16L, 8L, 16L, 16L, 16L, 8L, 1L, 
1L, 23L, 14L, 1L, 23L, 1L, 23L, 1L, 3L, 7L, 3L, 19L, 3L, 9L, 
9L, 9L, 6L, 9L, 16L, 16L, 8L, 1L, 23L, 1L, 23L, 14L, 3L, 3L, 
7L, 7L, 9L, 11L, 11L, 16L, 16L, 8L, 21L, 23L, 1L, 23L, 19L, 3L, 
19L, 19L, 19L, 19L, 6L, 6L, 6L, 11L, 11L, 6L, 6L, 6L, 6L, 9L, 
9L, 6L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 1L, 14L, 1L, 
10L, 13L, 10L, 10L, 13L, 13L, 13L, 10L, 20L, 24L, 24L, 5L, 20L, 
20L, 20L, 5L, 20L, 24L, 5L, 5L, 20L, 12L, 17L, 12L, 12L, 12L, 
15L, 12L, 12L, 12L, 17L, 17L, 17L, 12L, 15L, 15L, 12L, 12L, 17L, 
12L, 15L, 17L, 12L, 12L, 12L, 12L, 12L, 22L, 22L, 2L, 4L, 22L, 
2L, 22L, 2L, 13L, 18L, 13L, 5L, 5L, 12L, 17L, 22L, 22L, 22L, 
22L, 13L, 13L, 18L, 18L, 18L, 20L, 20L, 20L, 20L, 5L, 5L, 5L, 
5L, 24L, 5L, 5L, 12L, 17L, 17L, 12L, 17L, 12L, 4L, 10L, 13L, 
18L, 13L, 10L, 5L, 5L, 24L, 20L, 20L, 20L, 5L, 20L, 24L, 12L, 
17L, 12L, 17L, 17L, 12L, 17L, 22L, 22L), levels = c("10C1", "10C2", 
"11C1", "11UV2", "12C2", "12UV1", "13G1", "14UV1", "16C1", "1UV2", 
"2G1", "2G2", "3C2", "4UV1", "4UV2", "5C1", "5C2", "6G2", "6UV1", 
"7G2", "8G1", "8G2", "9G1", "9UV2"), class = "factor"), treat = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 
    1L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 
    3L, 3L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 
    1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 3L, 1L, 2L, 1L, 
    2L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 
    1L, 2L, 1L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 3L, 
    2L, 2L, 1L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 
    3L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 1L, 
    2L, 2L, 2L, 1L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 
    2L, 2L, 2L, 1L, 1L, 1L, 2L, 3L, 3L, 2L, 2L, 1L, 2L, 3L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 
    1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 
    2L, 3L, 3L, 1L, 2L, 1L, 3L, 1L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 
    3L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L), levels = c("C", 
    "G", "UV"), class = "factor"), native_driftertype = 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, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), levels = c("native", 
    "Resident", "Transient", "Voyeur", "unknown"), class = "factor"), 
    replica = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 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, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L), levels = c("1", "2"), class = "factor"), 
    compartment = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 
    2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 
    1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 
    2L, 2L, 2L, 1L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 
    2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 3L, 
    1L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 1L, 3L, 3L, 2L, 3L, 
    1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 
    2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 
    1L, 3L, 3L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 1L, 
    2L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 1L, 
    1L, 1L, 2L, 3L, 3L, 2L, 2L, 1L, 2L, 3L, 1L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 
    1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
    1L, 1L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 3L, 3L, 1L, 
    2L, 1L, 3L, 1L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 1L, 2L, 
    1L, 1L, 2L, 1L, 2L, 2L), levels = c("12", "14", "16"), class = "factor")), row.names = c(NA, -464L
), class = "data.frame")

I tried using the brm function (Fit Bayesian Generalized (Non-)Linear Multivariate Multilevel Models) from the brms package, but when running the following model, I get the following error:

fit=brm(native_driftertype~treat+(1|replica)+(1|compartment/origin_hive), family = multinomial(), data = data6)

Error: At least 2 response categories are required.

I also tried the mclogit function with the following model, but get the following error:

mclogit(native_driftertype~treat,
        random = list(~1|replica, ~1|compartment/origin_hive), data=data6)

Error in formula[[2]][[1]] : object of type 'symbol' is not subsettable

I cannot reproduce the error that you get but I did receive the error *Error: At least 2 response categories are required* . From a quick look at [this](https://discourse.mc-stan.org/t/example-with-family-multinomial/8707/7) suggests that you will need to tweak your formula (perhaps `data6$y = model.matrix(~native_driftertype-1, data=data6) ; brm(bf(y | trials(1L) ~ treat+(1|replica)+(1|compartment/origin_hive)), family = multinomial, data = data6)`) — user20650, Mar 08 '23 at 21:04
Kaat; re your updated error. Please see the link in the above comment (and perhaps the code). You need to transofrm your data e.g. you cannot just pass a single coloumn as your outcome. — user20650, Mar 11 '23 at 19:50

Multinomial mixed effect model in R

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