Any suggestions on how to best implement a power analysis in R when having a binomial generalized linear mixed model (glmm) with 2 categorical predictors as fixed effects (2 levels and 8 factor levels) and testing hypotheses through model comparison with Likelihood-Ratio-Tests (LRT) are welcome. My hypothesis is e.g.: Does the null model explain the data better than the model with fixed effect "X"? Which I'll test by comparing two models using a LRT with the command anova(m0, m1). The package mixedpower gives the option to simulate estimates of parameters of lme4::glmers which is rather useful. Though I'm wondering (given our hypohesis and how we want to test it), if a simulation-based power analysis of the LRT between models would be a better choice than doing a simulation-based power analysis on one full model, what this package seems to offer only. I am looking for any suggestions on how to implement that (preferably easy to use packages) and I'm glad about any comments regarding my line of thought (e.g. if this is even possible, if a power analysis using mixedpower() on one full model and looking at the estimates is a better/equally good option and/or if what I'm asking for is overcomplicating things?).
Best, Sahila