Robust SEs in GLMMs

You have to be careful when modeling auto-correlation in a binary
response.  When using a Gaussian distribution it is possible to model the
variance and correlation separately from the mean.  No so for a Bernoulli
distribution (or binomial or Poisson).  In some sense the whole purpose of
generalized linear models is to take into account that the variance of each
response is determined by its mean in these distributions.

glmmPQL is a wrapper around the lme function from the nlme package. But
lme, which provides for modelling correlations, was not intended for this
purpose.  I personally don't think it would make sense to use a correlation
function with a binary response.

A preferred approach is to incorporate Gaussian-distributed random effects
that have the desired auto-correlation pattern.


On Tue Nov 25 2014 at 11:58:08 AM Tim Meehan <tmeeha at gmail.com> wrote:

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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