[R-sig-ME] pvalues & model inference

Stephanie Rivest @rive046 @ending from uott@w@@c@
Thu Nov 1 16:59:55 CET 2018

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
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.


Stephanie Rivest
Ph.D. Candidate | Candidate au Doctorat
Dept. of Biology | Dép. de Biologie
University of Ottawa | Université d'Ottawa

	[[alternative HTML version deleted]]

More information about the R-sig-mixed-models mailing list