I am in my first experience using mixed models in R for my statistical analysis. Due to my data being comprised of binary outcome variables, I have managed to build a logistic model using the glmer function of the lme4 package that I think works as I wanted it to.

I am now aiming to investigate the statistical significance of my model coefficients. I have read that generally, the best approach for generalized mixed models is to bootstrap confidence intervals, but I haven't managed to find a good, clear, explanation of how to do this in R.

Would anyone have any suggestions? Are there any packages in R that expedite this process, or do people generally build their own functions for this? I haven't really done any bootstrapping before so I'd appreciate some more in-depth answers.

CodePudding user response：

If you want to compute parametric bootstrap confidence intervals, the built-in functionality

confint(fitted_model, method = "boot")

should work (see ?confint.merMod)

Also see this answer (which illustrates both parametric and nonparametric bootstrapping for user-defined quantities).

If you have multiple cores, you can speed this up by adding parallel = "multicore", ncpus = parallel::detectCores()-1 (or some other appropriate number of cores to use): see ?lme4::bootMer for details.