Missing standard errors and confidence for mixed model and ggeffects - r

Question

I'm trying to use ggeffects::ggpredict to make some effects plots for my model. I find that the standard errors and confidence limits are missing for many of the results. I can reproduce the problem with some simulated data. It seems specifically for observations where the standard error puts the predicted probability close to 0 or 1.
I tried to get predictions on the link scale to diagnose if it's a problem with the translation from link to response, but I don't believe this is supported by the package.
Any ideas how to address this? Many thanks.
library(tidyverse)
library(lme4)
library(ggeffects)
# number of simulated observations
n <- 1000
# simulated data with a numerical predictor x, factor predictor f, response y
# the simulated effects of x and f are somewhat weak compared to the noise, so expect high standard errors
df <- tibble(
x = seq(-0.1, 0.1, length.out = n),
g = floor(runif(n) * 3),
f = letters[1 + g] %>% as.factor(),
y = pracma::sigmoid(x + (runif(n) - 0.5) + 0.1 * (g - mean(g))),
z = if_else(y > 0.5, "high", "low") %>% as.factor()
)
# glmer model
model <- glmer(z ~ x + (1 | f), data = df, family = binomial)
print(summary(model))
#> Generalized linear mixed model fit by maximum likelihood (Laplace
#> Approximation) [glmerMod]
#> Family: binomial ( logit )
#> Formula: z ~ x + (1 | f)
#> Data: df
#>
#> AIC BIC logLik deviance df.resid
#> 1373.0 1387.8 -683.5 1367.0 997
#>
#> Scaled residuals:
#> Min 1Q Median 3Q Max
#> -1.3858 -0.9928 0.7317 0.9534 1.3600
#>
#> Random effects:
#> Groups Name Variance Std.Dev.
#> f (Intercept) 0.0337 0.1836
#> Number of obs: 1000, groups: f, 3
#>
#> Fixed effects:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 0.02737 0.12380 0.221 0.825
#> x -4.48012 1.12066 -3.998 6.39e-05 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Correlation of Fixed Effects:
#> (Intr)
#> x -0.001
# missing standard errors
ggpredict(model, c("x", "f")) %>% print()
#> Data were 'prettified'. Consider using `terms="x [all]"` to get smooth plots.
#> # Predicted probabilities of z
#>
#> # f = a
#>
#> x | Predicted | 95% CI
#> --------------------------------
#> -0.10 | 0.62 | [0.54, 0.69]
#> 0.00 | 0.51 |
#> 0.10 | 0.40 |
#>
#> # f = b
#>
#> x | Predicted | 95% CI
#> --------------------------------
#> -0.10 | 0.62 | [0.56, 0.67]
#> 0.00 | 0.51 |
#> 0.10 | 0.40 |
#>
#> # f = c
#>
#> x | Predicted | 95% CI
#> --------------------------------
#> -0.10 | 0.62 | [0.54, 0.69]
#> 0.00 | 0.51 |
#> 0.10 | 0.40 |
ggpredict(model, c("x", "f")) %>% as_tibble() %>% print(n = 20)
#> Data were 'prettified'. Consider using `terms="x [all]"` to get smooth plots.
#> # A tibble: 9 x 6
#> x predicted std.error conf.low conf.high group
#> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 -0.1 0.617 0.167 0.537 0.691 a
#> 2 -0.1 0.617 0.124 0.558 0.672 b
#> 3 -0.1 0.617 0.167 0.537 0.691 c
#> 4 0 0.507 NA NA NA a
#> 5 0 0.507 NA NA NA b
#> 6 0 0.507 NA NA NA c
#> 7 0.1 0.396 NA NA NA a
#> 8 0.1 0.396 NA NA NA b
#> 9 0.1 0.396 NA NA NA c
Created on 2022-04-12 by the reprex package (v2.0.1)

Answer 1

I think this may be due to the singular model fit.
I dug down into the guts of the code as far as here, where there appears to be a mismatch between the dimensions of the covariance matrix of the predictions (3x3) and the number of predicted values (15).
I further suspect that the problem may happen here:
rows_to_keep <- as.numeric(rownames(unique(model_matrix_data[
intersect(colnames(model_matrix_data), terms)])))
Perhaps the function is getting confused because the conditional modes/BLUPs for every group are the same (which will only be true, generically, when the random effects variance is zero) ... ?
This seems worth opening an issue on the ggeffects issues list ?