Is anyone familiar with this error message? I have a model that converged, but when I try to check for concurvity I repeatedly get this error. It occurs no matter which terms or how many I kick out. I know it would help if I had a reproducible example, but 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 a similar problem here, but it's solution doesn't seem to work for generalized additive mixed models. I have also tried setting the `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).