Robust SEs in GLMMs
Hi Sharon, I just looked over a paper by Bolker et al. (2008. GLMMs: a practical guide for ecology and evolution. TREE). Turns out that while it is possible to model binary data with glmmPQL, it's not really recommended. Nonetheless, you might look for other options that involve modeling autocorrelation rather than correcting for it after the fact. Best, Tim
On Tue, Nov 25, 2014 at 10:19 AM, Tim Meehan <tmeeha at gmail.com> wrote:
Hi Sharon, Take a look at glmmPQL in the MASS package. This function allows you to model a binary response, with random effects, and temporally and spatially correlated errors. If you model the correlations, there is less of a need for adjusting standard errors. Best, Tim On Sun, Nov 23, 2014 at 2:04 PM, Sharon Poessel <sharpoes at gmail.com> wrote:
When computing resource selection functions for animal telemetry data with
a binary response variable, where the 1s represent animal location data,
which are spatially and temporally correlated, and the 0s represent random
locations, which are not correlated, it is recommended to calculate
robust,
or empirical, standard errors instead of using the model-based standard
errors to account for this differing correlation structure. As far as I
can tell, none of the glmm packages in R calculate these robust SEs. Does
anyone know of a way to use glmms that calculate these? Thanks.
Sharon
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