I have a model that converged, but when I try to check for concurvity (used "multicollinearity" as it's the only working tag) I repeatedly get this error message. It occurs no matter which terms or how many I kick out. My data is 1,500 rows x 23 columns and I can't reproduce the error with a smaller subset of data. I've seen this problem here, but it's solution doesn't seem to work for generalized additive mixed models. I have also tried setting full argument to TRUE and FALSE.
library(mgcv)
mod <- bam(num ~
# Parametric terms
CYR.std * Season +
# Habitat covariates
sed_depth * ave_hw +
total_ave_ma +
s(ave_tt) +
# Structural components
s(Site, bs = "re") +
s(Site, fCYR, bs = "re") +
s(Site, CYR.std, bs = "re") +
s(Season, fCYR, bs = "re") +
offset(log(area_sampled)),
data = toad,
method = 'fREML',
discrete = TRUE,
family = poisson,
control = list(trace = TRUE))
Setup complete. Calling fit
Deviance = 226.280118812246 Iterations - 1
Deviance = 502.577417444367 Iterations - 2
Deviance = 576.624536857 Iterations - 3
Deviance = 685.39118817115 Iterations - 4
Deviance = 714.899394181739 Iterations - 5
Deviance = 735.575311982761 Iterations - 6
Deviance = 734.653307725508 Iterations - 7
Deviance = 735.874682895636 Iterations - 8
Deviance = 736.673618482559 Iterations - 9
Deviance = 736.861294501416 Iterations - 10
Deviance = 736.981664035411 Iterations - 11
Deviance = 737.00995853621 Iterations - 12
Deviance = 737.028133131768 Iterations - 13
Deviance = 737.032400712599 Iterations - 14
Deviance = 737.035137664603 Iterations - 15
Deviance = 737.035779699487 Iterations - 16
Deviance = 737.03619105456 Iterations - 17
Deviance = 737.036287496242 Iterations - 18
Deviance = 737.036349254884 Iterations - 19
Fit complete. Finishing gam object.
user.self sys.self elapsed
initial 0.02 0.00 0.01
gam.setup 0.03 0.00 0.03
pre-fit 0.00 0.00 0.00
fit 7.03 0.34 7.39
finalise 0.00 0.00 0.00
concurvity(mod)
Error in forwardsolve(t(Rt), t(R[1:r, , drop = FALSE])) : singular matrix in 'backsolve'. First zero in diagonal [14]
UPDATE:
Removing the random effect "s(fSeason, fCYR, bs = "re") +..." did the trick, but I don't understand why. I'm also not sure what to do if it's an important part of faithfully representing the survey design (random intercept that captures similarity of observations within a season within a year). Is this a "Cross Validated question" now?
Estimated variance components:
> gratia::variance_comp(mod)
# # A tibble: 5 × 5
component variance std_dev lower_ci upper_ci
<chr> <dbl> <dbl> <dbl> <dbl>
1 s(ave_tt) 0.000266 0.0163 0.00471 0.0564
2 s(fSite) 0.0817 0.286 0.0658 1.24
3 s(fCYR,fSite) 0.839 0.916 0.786 1.07
4 s(CYR.std,fSite) 0.00433 0.0658 0.0427 0.101
5 s(fSeason,fCYR) 0.667 0.816 0.565 1.18
example data:
> x <- toad[sample(nrow(toad), 1), ]
> dput(x)
structure(list(CYR = 2010L, Season = structure(2L, levels = c("DRY",
"WET"), class = "factor"), Site = structure(34L, 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 = 3L, Latitude = 25.48503,
Longitude = -80.33982, num = 0L, den = 0, occur = 0L, temp = 32.3,
DO = 2.7, sal = 28.78, group = "polyhaline", water_depth = 85L,
sed_depth = 54, ave_sav = 36, ave_tt = 0, ave_hw = 22.5,
ave_sf = 0, ave_rm = 0, total_ave_ma = 0.1, fCYR = structure(4L, levels = c("2007",
"2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015",
"2016", "2017", "2018", "2019", "2020", "2021", "2022"), class = "factor"),
CYR.std = 3L), row.names = 130183L, class = "data.frame")