Robust SEs in GLMMs
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
[[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models