[R-sig-ME] Robust SEs in GLMMs

[Tim Meehan]

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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>>
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>
>

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