UPDATE:
I even get the same error with the most basic of random effects...
m <- bam(num ~ s(sal) +
CYR.std +
fSeason +
s(fSite, bs = "re") +
s(fCYR, bs = "re"),
offset(log(area_sampled)),
method = "fREML",
discrete = TRUE,
family = nb(link = "log"),
data = x)
> mgcv::gam.vcomp(m)
Standard deviations and 0.95 confidence intervals:
std.dev lower upper
s(sal) 0.01623266 0.0105176 0.02505317
s(fSite) 0.83502554 0.3549272 1.96453684
s(fCYR) 0.54846495 0.3553660 0.84649014
Rank: 2/2
Warning messages:
1: In lsd - crit * sd.lsd :
longer object length is not a multiple of shorter object length
2: In lsd + crit * sd.lsd :
longer object length is not a multiple of shorter object length
UPDATE:
I even get the same error with the most basic of random effects...
m <- bam(num ~ s(sal) +
CYR.std +
fSeason +
s(fSite, bs = "re") +
s(fCYR, bs = "re"),
offset(log(area_sampled)),
method = "fREML",
discrete = TRUE,
family = nb(link = "log"),
data = x)
> mgcv::gam.vcomp(m)
Standard deviations and 0.95 confidence intervals:
std.dev lower upper
s(sal) 0.01623266 0.0105176 0.02505317
s(fSite) 0.83502554 0.3549272 1.96453684
s(fCYR) 0.54846495 0.3553660 0.84649014
Rank: 2/2
Warning messages:
1: In lsd - crit * sd.lsd :
longer object length is not a multiple of shorter object length
2: In lsd + crit * sd.lsd :
longer object length is not a multiple of shorter object length
Variance calculation warning: longer object length is not a multiple
I'm getting this warning after checking the variance components of my model. I don't have NA's and the warning goes away when I remove this s(CYR.std, fSite, bs = "fs") term. Is it possibly related to having only 1 observation associated with a fSite in a year (CYR.std)? I get the same warning with the full dataset.
gratia::variance_comp(m1)
# A tibble: 4 ×ばつ 5
component variance std_dev lower_ci upper_ci
<chr> <dbl> <dbl> <dbl> <dbl>
1 s(sal) 4.47e-10 0.0000211 0.00000968 0.0000462
2 s(CYR.std,fSite)1 3.92e- 2 0.198 0.112 0.352
3 s(CYR.std,fSite)2 6.13e- 1 0.783 0 Inf
4 s(CYR.std,fSite)3 3.43e- 7 0.000586 0.000268 0.00128
Warning messages:
1: In lsd - crit * sd.lsd :
longer object length is not a multiple of shorter object length
2: In lsd + crit * sd.lsd :
longer object length is not a multiple of shorter object length
Random subset of data recreates the error:
MyVar <- c("fCYR", "CYR.std", "fSeason", "fSite", "area_sampled", "num", "sal")
x <- shrimp2[MyVar][sample(nrow(shrimp2[MyVar]), 100), ]
na_count <-sapply(x, function(y) sum(length(which(is.na(y)))))
na_count <- data.frame(na_count)
na_count
na_count
fCYR 0
CYR.std 0
fSeason 0
fSite 0
area_sampled 0
num 0
sal 0
library(mgcv)
m1 <- bam(num ~ s(sal) +
CYR.std*fSeason +
s(CYR.std, fSite, bs = "fs"),
offset(log(area_sampled)),
method = "fREML",
discrete = TRUE,
family = nb(link = "log"),
data = x)
Full dataset (n=1327):
> table(shrimp2$CYR.std, shrimp2$fSite)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
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 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2
3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
5 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 1 2 2 2
6 2 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 1 1
7 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
8 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
9 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2
10 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
11 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
12 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
13 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
14 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
42 43 44 45 46 47
0 2 2 2 1 2 2
1 2 2 2 2 2 2
2 2 1 1 1 1 1
3 2 2 2 2 2 2
4 2 2 2 2 2 2
5 2 2 2 2 2 2
6 1 1 1 1 1 1
7 2 2 2 2 2 2
8 2 2 2 2 2 2
9 2 2 2 2 2 2
10 2 2 2 2 2 2
11 2 2 2 2 2 2
12 2 2 2 2 2 2
13 1 1 1 1 1 1
14 2 2 2 2 2 2
Smaller dataset:
> dput(x)
structure(list(fCYR = structure(c(9L, 9L, 12L, 15L, 11L, 11L,
8L, 11L, 2L, 9L, 1L, 14L, 13L, 10L, 3L, 15L, 14L, 6L, 7L, 1L,
9L, 8L, 6L, 8L, 2L, 4L, 3L, 6L, 7L, 15L, 11L, 1L, 2L, 9L, 5L,
5L, 2L, 6L, 11L, 6L, 13L, 12L, 9L, 6L, 6L, 6L, 7L, 5L, 1L, 12L,
1L, 14L, 14L, 5L, 8L, 2L, 4L, 1L, 15L, 3L, 8L, 12L, 12L, 15L,
4L, 11L, 5L, 1L, 9L, 6L, 4L, 7L, 15L, 7L, 3L, 6L, 11L, 14L, 3L,
10L, 11L, 13L, 7L, 4L, 11L, 3L, 10L, 10L, 9L, 1L, 10L, 10L, 1L,
10L, 9L, 9L, 7L, 15L, 11L, 14L), levels = c("2008", "2009", "2010",
"2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018",
"2019", "2020", "2021", "2022"), class = "factor"), CYR.std = c(8L,
8L, 11L, 14L, 10L, 10L, 7L, 10L, 1L, 8L, 0L, 13L, 12L, 9L, 2L,
14L, 13L, 5L, 6L, 0L, 8L, 7L, 5L, 7L, 1L, 3L, 2L, 5L, 6L, 14L,
10L, 0L, 1L, 8L, 4L, 4L, 1L, 5L, 10L, 5L, 12L, 11L, 8L, 5L, 5L,
5L, 6L, 4L, 0L, 11L, 0L, 13L, 13L, 4L, 7L, 1L, 3L, 0L, 14L, 2L,
7L, 11L, 11L, 14L, 3L, 10L, 4L, 0L, 8L, 5L, 3L, 6L, 14L, 6L,
2L, 5L, 10L, 13L, 2L, 9L, 10L, 12L, 6L, 3L, 10L, 2L, 9L, 9L,
8L, 0L, 9L, 9L, 0L, 9L, 8L, 8L, 6L, 14L, 10L, 13L), fSeason = structure(c(2L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 2L), levels = c("DRY", "WET"), class = "factor"), fSite = structure(c(18L,
33L, 27L, 15L, 47L, 8L, 3L, 29L, 34L, 6L, 44L, 8L, 11L, 14L,
4L, 43L, 19L, 39L, 15L, 47L, 24L, 36L, 19L, 39L, 39L, 33L, 16L,
35L, 40L, 11L, 42L, 31L, 40L, 25L, 6L, 35L, 38L, 16L, 34L, 42L,
47L, 9L, 27L, 11L, 29L, 35L, 36L, 46L, 11L, 21L, 1L, 31L, 10L,
32L, 6L, 22L, 42L, 25L, 44L, 28L, 15L, 20L, 8L, 14L, 23L, 6L,
8L, 32L, 2L, 45L, 31L, 20L, 7L, 37L, 46L, 39L, 36L, 35L, 4L,
1L, 19L, 16L, 2L, 4L, 23L, 20L, 10L, 27L, 15L, 34L, 40L, 8L,
13L, 26L, 39L, 43L, 34L, 39L, 22L, 46L), levels = c("1", "2",
"3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14",
"15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25",
"26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36",
"37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47"
), class = "factor"), area_sampled = c(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, 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, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), num = c(0L,
1L, 0L, 0L, 2L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 2L, 0L, 0L, 2L, 0L,
0L, 0L, 0L, 0L, 3L, 0L, 19L, 3L, 2L, 0L, 0L, 0L, 0L, 1L, 1L,
2L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 1L, 2L, 0L, 0L, 1L, 0L, 1L, 0L,
3L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
6L, 0L, 0L, 1L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L,
1L, 0L, 0L, 0L, 2L, 1L, 0L, 1L, 0L, 1L, 0L, 3L, 0L, 0L, 0L, 1L,
0L, 2L, 2L, 1L), sal = c(4.78, 18.55, 19.72, 25.08, 16.48, 15.47,
24.88, 15.12, 25.69, 23.64, 33.58, 24.37, 31.77, 28.98, 21.14,
25.37, 20, 23.03, 27.01, 27.46, 8.06, 29.7, 27.44, 30.41, 21.13,
22.47, 16.8, 23.54, 37.1, 30.16, 29.26, 22.08, 31.68, 10.56,
19.1, 14.73, 29.34, 17.77, 20.07, 32.71, 30.47, 27.76, 11.26,
31.8, 26.85, 10.79, 17.31, 26.85, 20.62, 26.58, 30.46, 23.9,
23.01, 22.3, 20.49, 18.19, 29.74, 21.27, 23.74, 25.42, 23.46,
26.33, 30.99, 29.99, 29.77, 21, 29, 21.37, 15.83, 32.36, 33.52,
24.76, 28.48, 17.23, 33.79, 19.89, 2.79, 21.75, 29.98, 15.18,
13.48, 29.94, 27.39, 37.13, 4.04, 21.7, 11.29, 6.3, 11.37, 21.44,
19.82, 10.03, 22.73, 21.73, 8.76, 13.49, 18.21, 35.25, 4.15,
25.88)), row.names = c(268512L, 258208L, 329600L, 387560L, 312120L,
309912L, 253424L, 298136L, 109720L, 264096L, 75128L, 381304L,
341008L, 290224L, 128304L, 383880L, 373760L, 198776L, 235208L,
65376L, 258576L, 238336L, 203192L, 245328L, 89664L, 142104L,
134560L, 202088L, 227112L, 393080L, 296112L, 65744L, 110824L,
258760L, 195280L, 187736L, 110456L, 211656L, 299056L, 199328L,
337144L, 335120L, 266304L, 204664L, 200984L, 210552L, 220488L,
172096L, 83040L, 323528L, 62800L, 375968L, 379280L, 173200L,
254160L, 107512L, 152592L, 68872L, 384064L, 132720L, 252136L,
323344L, 317456L, 395840L, 164552L, 309360L, 177248L, 65928L,
271456L, 199880L, 162344L, 231344L, 386088L, 220672L, 138792L,
207792L, 310096L, 376704L, 122048L, 275320L, 299976L, 342296L,
225272L, 166024L, 315248L, 120392L, 275504L, 282864L, 267960L,
66296L, 286544L, 276608L, 83408L, 288200L, 256736L, 264832L,
220120L, 402096L, 315064L, 378912L), class = "data.frame")
lang-r