I've built a relatively large negative binomial GLMM (~50000 observations, 10 covariates in conditional model, 1 covariate as zero-inflation model, 3 random intercepts (650, 26, 26 levels in each group respectively), and a number of random slopes). The model was fitted with the package glmmTMB
in R.
I'm trying to build confidence intervals for my covariates, and I'm trying to figure out the advantages of building them by likelihood profile vs bootstrapping. Is there a reason to use one over the other?
Best Answer
tl;dr parametric bootstrap intervals are slightly more reliable, but much slower to compute. I would guess that either would be adequate in your case.
glmmTMB
(unlikelme4
), although there is asimulate()
method for fitted models, so it shouldn't be too hard to put one together if you know what you're doing.