Robust SEs in GLMMs

It is not too hard to set up a cluster bootstrap (i.e. clusters are
sampled) for clustered data, although I'm not certain what happens with
spatial data. It then takes a bit of time to run.

I've used it where I had additional possible random effects and was
concerned about fitting models with that many random effects. Nicely,
results for models with 5 random effects fitted with nlme gave the same
results for the fixed effects as the 3 random effect models with nlme with
the clustered bootstrap.

On 26 November 2014 at 09:10, Ben Bolker <bbolker at gmail.com> wrote:

I hate to sound contrary, but ... I actually think that implementing
the robust standard errors would be the best way to go here.  I don't
have time to work on it myself right now, but someone reasonably
experienced in R should be able to look at the `sandwich` package and
figure out how to write "bread" and "meat" methods for `merMod`
objects ...

On Tue, Nov 25, 2014 at 2:11 PM, Tim Meehan <tmeeha at gmail.com> wrote:

Thanks for clarifying the problem with correlation functions and binary
responses, Doug.  Regarding the random effects approach, how would one

set

that up?  Would you divide the data into spatial or temporal blocks, and
use the blocks in the random statement, for example?

On Tue, Nov 25, 2014 at 11:16 AM, Douglas Bates <bates at stat.wisc.edu>

wrote:

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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