[R-sig-ME] Robust SEs in GLMMs

[Douglas Bates]

Ben is better able to respond on this than I am.  For GLMMs it is possible
to use a random effect that has one component per observation.  This is
where the spatial or temporal correlation would be incorporated.

On Tue Nov 25 2014 at 1:12:45 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
>>> >>
>>> >>         [[alternative HTML version deleted]]
>>> >>
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>>> >>
>>> >
>>> >
>>>
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>>>
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>>
>

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