[R-sig-ME] pvalues & model inference
Anthony R. Ives
@rive@ @ending from wi@c@edu
Thu Nov 1 17:30:05 CET 2018
The short answer is that a bootstrap can address both bias (problems with estimates) and hypothesis testing. It is often the case that, even though estimates are unbiased, a bootstrap is still needed for hypothesis testing.
If you are interested in p-values, I'd perform bootstraps under the null hypothesis. Standard bootstraps are generally performed using the estimates, and if there is bias, this can still give you incorrect p-values.
There are several examples of different types of bootstraps and randomizations here: https://leanpub.com/correlateddata
Anthony R. Ives
459 Birge Hall
From: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> on behalf of Stephanie Rivest <srive046 using uottawa.ca>
Date: Thursday, November 1, 2018 at 11:03 AM
To: "r-sig-mixed-models using r-project.org" <r-sig-mixed-models using r-project.org>
Subject: [R-sig-ME] pvalues & model inference
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.
Ph.D. Candidate | Candidate au Doctorat
Dept. of Biology | Dép. de Biologie
University of Ottawa | Université d'Ottawa
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