Derek Corcoran
Reputation: 4102
Error in emmeans for averaging object (mth$objs[[1]])(object, trms, xlev, grid, ...): Unable to match model terms
emmeans can't evaluate averaging object
The problem is that emmeans gives an error when evaluating an averaging object form the MuMIn package, even when it says it should in this link I have been trying to debug this for a couple of days with no luck. All data and code is in this repo
first we load the needed r packages and the dataset
library(emmeans)
library(lme4)
#;-) Loading required package: Matrix
library(MuMIn)
library(doParallel)
#;-) Loading required package: foreach
#;-) Loading required package: iterators
#;-) Loading required package: parallel
Data <- readRDS("Data.rds")
If you dont want to download the data you can use this
Data <- structure(list(richness = c(25L, 24L, 14L, 13L, 11L, 16L, 10L,
27L, 31L, 34L, 20L, 25L, 23L, 8L, 10L, 7L, 9L, 13L, 15L, 23L,
22L, 20L, 22L, 15L, 35L, 18L, 15L, 36L, 29L, 35L, 32L, 24L, 22L,
18L, 14L, 17L, 22L, 34L, 30L, 15L, 17L, 23L, 24L, 6L, 9L, 10L,
8L, 4L, 5L, 21L, 24L, 17L, 11L, 13L, 13L, 11L, 25L, 20L, 21L,
9L, 20L, 16L, 7L, 9L, 6L, 8L, 11L, 12L, 16L, 19L, 13L, 15L, 14L,
22L, 8L, 6L, 23L, 17L, 27L, 31L, 12L, 12L, 15L, 15L, 10L, 13L,
29L, 32L, 14L, 12L, 20L, 22L, 6L, 8L, 9L, 5L, 3L, 4L, 14L, 31L,
19L, 11L, 13L, 17L, 12L, 21L, 16L, 21L, 24L, 15L, 14L, 10L, 10L,
11L, 13L, 12L, 18L, 17L, 14L, 17L, 11L, 17L, 24L, 14L, 7L, 29L,
27L, 31L, 37L, 17L, 17L, 14L, 12L, 26L, 21L, 27L, 19L, 17L, 11L,
20L, 17L, 6L, 11L, 11L, 6L, 3L, 5L, 24L, 20L, 17L, 14L, 15L,
12L, 11L, 21L, 21L, 18L, 11L, 26L, 15L, 10L, 9L, 8L, 9L, 13L,
17L, 6L, 12L, 19L, 9L, 20L, 15L, 9L, 10L, 30L, 26L, 39L, 31L,
18L, 20L, 16L, 11L, 27L, 22L, 29L, 21L, 17L, 14L, 27L, 17L, 5L,
7L, 10L, 6L, 2L, 4L, 25L, 18L, 19L, 12L, 12L, 14L, 16L, 26L,
15L, 24L, 11L, 26L, 21L, 10L, 8L, 7L, 8L, 10L, 14L, 8L, 10L,
13L, 14L, 15L, 14L, 13L, 9L, 34L, 26L, 41L, 27L, 16L, 17L, 14L,
26L, 18L, 29L, 17L, 19L, 13L, 22L, 19L, 8L, 7L, 8L, 7L, 2L, 5L
), aspect = c(200, 186, 138, 152, 158, 326, 332, 150, 151, 126,
63, 110, 180, 302, 12, 164, 146, 32, 212, 152, 160, 124, 11,
102, 60, 180, 129, 89, 92, 100, 260, 94, 100, 0, 0, 0, 0, 156,
101, 0, 0, 0, 0, 82, 152, 164, 268, 116, 268, 200, 186, 138,
152, 158, 326, 332, 150, 151, 126, 63, 110, 180, 302, 12, 164,
146, 32, 212, 152, 160, 124, 11, 102, 60, 180, 129, 89, 92, 100,
260, 94, 100, 0, 0, 0, 0, 156, 101, 0, 0, 0, 0, 82, 152, 164,
268, 116, 268, 200, 186, 138, 152, 158, 326, 332, 150, 151, 126,
63, 110, 180, 302, 12, 164, 146, 32, 212, 152, 160, 124, 11,
102, 60, 180, 129, 89, 92, 100, 260, 94, 100, 0, 0, 0, 0, 156,
101, 0, 0, 0, 0, 82, 152, 164, 268, 116, 268, 200, 186, 138,
152, 158, 326, 332, 150, 151, 126, 63, 110, 180, 302, 12, 164,
146, 32, 212, 152, 160, 124, 11, 102, 60, 180, 129, 89, 92, 100,
260, 94, 100, 0, 0, 0, 0, 156, 101, 0, 0, 0, 0, 82, 152, 164,
268, 116, 268, 200, 186, 138, 152, 158, 326, 332, 150, 151, 126,
63, 110, 180, 302, 12, 164, 146, 32, 212, 152, 160, 124, 11,
102, 60, 180, 129, 89, 92, 100, 260, 94, 100, 0, 0, 0, 156, 101,
0, 0, 0, 0, 82, 152, 164, 268, 116, 268), elevation = c(59.639,
60.455, 49.532, 50.521, 52.628, 41.467, 39.91, 52.057, 55.861,
61.056, 60.571, 38.707, 40.645, 25.855, 32.852, 30.79, 26.7344,
25.8817, 27.277, 63.331, 62.715, 72.395, 74.567, 70.733, 68.974,
62.814, 62.708, 48.962, 49.978, 50.261, 49.805, 47.82, 46.711,
3.256, 3.197, 3.109, 3.209, 59.102, 59.51, 3.024, 2.971, 2.953,
3.106, 4.612, 2.43366667, 15.355, 2.091, 4.573, 4.563, 59.639,
60.455, 49.532, 50.521, 52.628, 41.467, 39.91, 52.057, 55.861,
61.056, 60.571, 38.707, 40.645, 25.855, 32.852, 30.79, 26.7344,
25.8817, 27.277, 63.331, 62.715, 72.395, 74.567, 70.733, 68.974,
62.814, 62.708, 48.962, 49.978, 50.261, 49.805, 47.82, 46.711,
3.256, 3.197, 3.109, 3.209, 59.102, 59.51, 3.024, 2.971, 2.953,
3.106, 4.612, 2.43366667, 15.355, 2.091, 4.573, 4.563, 59.639,
60.455, 49.532, 50.521, 52.628, 41.467, 39.91, 52.057, 55.861,
61.056, 60.571, 38.707, 40.645, 25.855, 32.852, 30.79, 26.7344,
25.8817, 27.277, 63.331, 62.715, 72.395, 74.567, 70.733, 68.974,
62.814, 62.708, 48.962, 49.978, 50.261, 49.805, 47.82, 46.711,
3.256, 3.197, 3.109, 3.209, 59.102, 59.51, 3.024, 2.971, 2.953,
3.106, 4.612, 2.43366667, 15.355, 2.091, 4.573, 4.563, 59.639,
60.455, 49.532, 50.521, 52.628, 41.467, 39.91, 52.057, 55.861,
61.056, 60.571, 38.707, 40.645, 25.855, 32.852, 30.79, 26.7344,
25.8817, 27.277, 63.331, 62.715, 72.395, 74.567, 70.733, 68.974,
62.814, 62.708, 48.962, 49.978, 50.261, 49.805, 47.82, 46.711,
3.256, 3.197, 3.109, 3.209, 59.102, 59.51, 3.024, 2.971, 2.953,
3.106, 4.612, 2.43366667, 15.355, 2.091, 4.573, 4.563, 59.639,
60.455, 49.532, 50.521, 52.628, 41.467, 39.91, 52.057, 55.861,
61.056, 60.571, 38.707, 40.645, 25.855, 32.852, 30.79, 26.7344,
25.8817, 27.277, 63.331, 62.715, 72.395, 74.567, 70.733, 68.974,
62.814, 62.708, 48.962, 49.978, 50.261, 49.805, 47.82, 46.711,
3.197, 3.109, 3.209, 59.102, 59.51, 3.024, 2.971, 2.953, 3.106,
4.612, 2.43366667, 15.355, 2.091, 4.573, 4.563), initial_habitat = c("Rangeland",
"Rangeland", "Forest", "Forest", "Forest", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Forest", "Forest", "Forest", "Forest", "Forest",
"Forest", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Meadow", "Meadow", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Meadow", "Meadow", "Meadow", "Meadow", "Forest", "Forest", "Forest",
"Forest", "Forest", "Forest", "Rangeland", "Rangeland", "Forest",
"Forest", "Forest", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Forest",
"Forest", "Forest", "Forest", "Forest", "Forest", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Meadow", "Meadow", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Meadow", "Meadow", "Meadow",
"Meadow", "Forest", "Forest", "Forest", "Forest", "Forest", "Forest",
"Rangeland", "Rangeland", "Forest", "Forest", "Forest", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Forest", "Forest", "Forest", "Forest",
"Forest", "Forest", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Meadow", "Meadow", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Meadow", "Meadow", "Meadow", "Meadow", "Forest", "Forest", "Forest",
"Forest", "Forest", "Forest", "Rangeland", "Rangeland", "Forest",
"Forest", "Forest", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Forest",
"Forest", "Forest", "Forest", "Forest", "Forest", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Meadow", "Meadow", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Meadow", "Meadow", "Meadow",
"Meadow", "Forest", "Forest", "Forest", "Forest", "Forest", "Forest",
"Rangeland", "Rangeland", "Forest", "Forest", "Forest", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Forest", "Forest", "Forest", "Forest",
"Forest", "Forest", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Rangeland", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Meadow", "Rangeland", "Rangeland", "Rangeland", "Rangeland",
"Meadow", "Meadow", "Meadow", "Meadow", "Forest", "Forest", "Forest",
"Forest", "Forest", "Forest"), year = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4), slope = c(5, 4, 20, 16, 10, 12, 16, 8, 11,
1, 5, 8, 8, 4, 10, 8, 8, 12, 6, 16, 21, 4, 1.5, 4, 5, 1, 2, 11,
10, 8, 1, 8, 8, 0, 0, 0, 0, 4, 6, 0, 0, 0, 0, 6, 4, 2, 1, 2,
6, 5, 4, 20, 16, 10, 12, 16, 8, 11, 1, 5, 8, 8, 4, 10, 8, 8,
12, 6, 16, 21, 4, 1.5, 4, 5, 1, 2, 11, 10, 8, 1, 8, 8, 0, 0,
0, 0, 4, 6, 0, 0, 0, 0, 6, 4, 2, 1, 2, 6, 5, 4, 20, 16, 10, 12,
16, 8, 11, 1, 5, 8, 8, 4, 10, 8, 8, 12, 6, 16, 21, 4, 1.5, 4,
5, 1, 2, 11, 10, 8, 1, 8, 8, 0, 0, 0, 0, 4, 6, 0, 0, 0, 0, 6,
4, 2, 1, 2, 6, 5, 4, 20, 16, 10, 12, 16, 8, 11, 1, 5, 8, 8, 4,
10, 8, 8, 12, 6, 16, 21, 4, 1.5, 4, 5, 1, 2, 11, 10, 8, 1, 8,
8, 0, 0, 0, 0, 4, 6, 0, 0, 0, 0, 6, 4, 2, 1, 2, 6, 5, 4, 20,
16, 10, 12, 16, 8, 11, 1, 5, 8, 8, 4, 10, 8, 8, 12, 6, 16, 21,
4, 1.5, 4, 5, 1, 2, 11, 10, 8, 1, 8, 8, 0, 0, 0, 4, 6, 0, 0,
0, 0, 6, 4, 2, 1, 2, 6), treatment = structure(c(2L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 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, 1L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
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, 1L, 2L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 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, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 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,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
1L), levels = c("PermanentExclosure", "Control"), class = "factor"),
block_no = structure(c(3L, 3L, 4L, 4L, 4L, 5L, 5L, 6L, 6L,
7L, 7L, 8L, 8L, 9L, 9L, 9L, 10L, 10L, 10L, 1L, 1L, 11L, 11L,
12L, 12L, 2L, 2L, 13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L,
17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 21L, 22L,
22L, 22L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L,
8L, 9L, 9L, 9L, 10L, 10L, 10L, 1L, 1L, 11L, 11L, 12L, 12L,
2L, 2L, 13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L, 17L,
18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 21L, 22L, 22L, 22L,
3L, 3L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L,
9L, 10L, 10L, 10L, 1L, 1L, 11L, 11L, 12L, 12L, 2L, 2L, 13L,
13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L, 17L, 18L, 18L, 19L,
19L, 20L, 20L, 21L, 21L, 21L, 22L, 22L, 22L, 3L, 3L, 4L,
4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 9L, 10L,
10L, 10L, 1L, 1L, 11L, 11L, 12L, 12L, 2L, 2L, 13L, 13L, 14L,
14L, 15L, 15L, 16L, 16L, 17L, 17L, 18L, 18L, 19L, 19L, 20L,
20L, 21L, 21L, 21L, 22L, 22L, 22L, 3L, 3L, 4L, 4L, 4L, 5L,
5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 9L, 10L, 10L, 10L, 1L,
1L, 11L, 11L, 12L, 12L, 2L, 2L, 13L, 13L, 14L, 14L, 15L,
15L, 16L, 17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L,
21L, 22L, 22L, 22L), levels = c("5", "6", "13", "15", "28",
"36", "37", "42", "46", "47", "54", "55", "60", "61", "62",
"69", "70", "74", "85", "95", "96", "97"), class = "factor")), row.names = c(NA,
-244L), class = c("tbl_df", "tbl", "data.frame"))
Now we fit a general model:
Model <- glmer(richness ~ aspect + elevation +
initial_habitat +
I(abs(year - 1)) +
I((year - 1)^2) +
slope +
treatment:initial_habitat +
year:initial_habitat +
year:treatment +
year:treatment:initial_habitat +
(1 | block_no), family = poisson, data = Data, control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)))
And we make a model selection (skip this step since it takes a while, the “SelectRichness.rds” file is in this github)
options(na.action = "na.fail")
library(doParallel)
cl <- makeCluster(4)
registerDoParallel(cl)
clusterEvalQ(cl, library(lme4))
#;-) [[1]]
#;-) [1] "lme4" "Matrix" "stats" "graphics" "grDevices" "utils"
#;-) [7] "datasets" "methods" "base"
#;-)
#;-) [[2]]
#;-) [1] "lme4" "Matrix" "stats" "graphics" "grDevices" "utils"
#;-) [7] "datasets" "methods" "base"
#;-)
#;-) [[3]]
#;-) [1] "lme4" "Matrix" "stats" "graphics" "grDevices" "utils"
#;-) [7] "datasets" "methods" "base"
#;-)
#;-) [[4]]
#;-) [1] "lme4" "Matrix" "stats" "graphics" "grDevices" "utils"
#;-) [7] "datasets" "methods" "base"
clusterExport(cl, "Data")
Select <- MuMIn::pdredge(Model, extra = list(R2m = function(x) r.squaredGLMM(x)[1, 1], R2c = function(x) r.squaredGLMM(x)[1, 2]), fixed = ~ YEAR:Treatment, cluster = cl)
stopCluster(cl)
saveRDS(Select, "SelectRichness.rds")
And now we Select the best models, I will do this twice, since the outcome of the subset function will be used to show how the best model does not have issues and the averaged model from get.models which is the result I need is not working
Select <- readRDS("SelectRichness.rds")
Selected <- subset(Select, delta < 2)
SelectedList <- get.models(Select, delta < 2)
Working with the best model works
As specified above the goal is to find if the treatments do yieald differences by year 4, based on the model. So first we will show this with the best model
BestModel <- get.models(Selected, 1)[[1]]
noise.emm <- emmeans(BestModel, pairwise ~ year + initial_habitat + initial_habitat:year + year:treatment, at = list(year = 4), data = Data)
pairs(noise.emm, simple = "treatment") |>
as.data.frame() |>
dplyr::filter(p.value < 0.05) |>
dplyr::arrange(initial_habitat, estimate) |>
dplyr::select(-SE, -df, -z.ratio) |>
knitr::kable()
| contrast | year | initial_habitat | estimate | p.value |
|---|---|---|---|---|
| PermanentExclosure - Control | 4 | Forest | -0.2193592 | 3.06e-05 |
| PermanentExclosure - Control | 4 | Meadow | -0.2193592 | 3.06e-05 |
| PermanentExclosure - Control | 4 | Rangeland | -0.2193592 | 3.06e-05 |
Working with the average model does not works
This does not work
AV <- model.avg(SelectedList, fit = TRUE)
noise.emm_av <- emmeans(AV, pairwise ~ year + initial_habitat + initial_habitat:year + year:treatment, at = list(year = 4), data = Data)
#;-) Error in (mth$objs[[1]])(object, trms, xlev, grid, ...): Unable to match model terms
-- nothing to show --
sessioninfo::session_info()
#;-) ─ Session info ───────────────────────────────────────────────────────────────
#;-) setting value
#;-) version R version 4.2.2 Patched (2022-11-10 r83330)
#;-) os Ubuntu 20.04.5 LTS
#;-) system x86_64, linux-gnu
#;-) ui X11
#;-) language en_US:en
#;-) collate en_US.UTF-8
#;-) ctype en_US.UTF-8
#;-) tz Europe/Copenhagen
#;-) date 2023-02-21
#;-) pandoc 2.19.2 @ /usr/lib/rstudio/bin/quarto/bin/tools/ (via rmarkdown)
#;-)
#;-) ─ Packages ───────────────────────────────────────────────────────────────────
#;-) package * version date (UTC) lib source
#;-) boot 1.3-28 2021-05-03 [4] CRAN (R 4.0.5)
#;-) cli 3.6.0 2023-01-09 [1] CRAN (R 4.2.2)
#;-) coda 0.19-4 2020-09-30 [3] CRAN (R 4.0.2)
#;-) codetools 0.2-19 2023-02-01 [4] CRAN (R 4.2.2)
#;-) digest 0.6.31 2022-12-11 [1] CRAN (R 4.2.2)
#;-) doParallel * 1.0.17 2022-02-07 [1] CRAN (R 4.2.1)
#;-) dplyr 1.1.0 2023-01-29 [1] CRAN (R 4.2.2)
#;-) emmeans * 1.8.4-1 2023-01-17 [1] CRAN (R 4.2.2)
#;-) estimability 1.4.1 2022-08-05 [1] CRAN (R 4.2.1)
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#;-) [4] /usr/lib/R/library
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