pvalues & model inference
Hi there, I am having some trouble understanding all the documentation that I've read regarding how to do hypothesis testing and model inference for a glmm with zero-inflation. I'm hoping someone can clarify. For a little background, I fit a model with the package glmmTMB for a response that is a count and is zero-inflated, random effects were included.
From what I understand, the Wald Z tests that are reported in the output of
a model fit with glmmTMB cannot be fully trusted for several reasons: (1) df are difficult to calculate, yet are used to do hypothesis testing, (2) Wald z tests make assumptions that can be violated (asymptotic null distributions), and (3) boundary effects can occur, especially for the random effects. To me, this sounds like the parameter estimates are ok, but the standard errors and p-values cannot be trusted. Therefore, its the *prediction intervals* that are incorrect, but not the estimates themselves. Is this interpretation right? I may have misinterpreted some of the terminology used as well, any guidance on this would be appreciated. I understand that a bootstrap is the next logical step, and my dataset is small enough that this option is feasible for me. What I don't understand is the purpose of the bootstrap. Is the aim to obtain more accurate prediction intervals and correct p-values? OR, are model estimates also made more reliable? Thanks in advance for taking the time to respond. Cheers, Stephanie Stephanie Rivest Ph.D. Candidate | Candidate au Doctorat Dept. of Biology | D?p. de Biologie University of Ottawa | Universit? d'Ottawa