# confidence intervals for fixed effects with reML

I am trying to asses wheter certain effects are significant in my mixed model. First I tried the confint function with the profile method and the Wald method. Both are somewhat similaire, now I want to perform a parametric bootstrap and I was wondering why isn't it possible to do this with a reML model? the package I use: PBmodcomp sets reML to false.

This: Kenward-Roger and REML didn't really help me further, why is it nonsenical to use a reML model, I thought reML was needed to get BLUE's?

There's a fairly subtle distinction here between fitting reduced models, which is what PBmodcomp() does and what you want to do to get p-values etc., and fitting models with varying values of a focal parameter, which is what confint() and friends do.
• Taking a REML model and re-evaluating/refitting it if we change the values of the focal (and other) parameters but don't set them specifically to zero is fine; this doesn't change the structure of the model. (Although I see that we do convert the REML fit to ML when profiling over the fixed effects in lme4 ... hmmm ...)
In particular, if you're looking to compute confidence intervals for a [g]lmer fit by parametric bootstrapping, you should use confint(., method="boot") rather than pbkrtest::PBmodcomp. This doesn't actually do anything with a reduced model; rather, it simulates values from the full model and re-fits.