[R-sig-ME] Robust SEs in GLMMs
Tim Meehan
tmeeha at gmail.com
Tue Nov 25 23:52:33 CET 2014
No contrariness taken. You know this stuff better than most, and we really
appreciate the time you take answering peoples questions.
On Tue, Nov 25, 2014 at 3:10 PM, 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
> >>> >>
> >>> >> [[alternative HTML version deleted]]
> >>> >>
> >>> >> _______________________________________________
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> >>> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>> >>
> >>> >
> >>> >
> >>>
> >>> [[alternative HTML version deleted]]
> >>>
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> >>>
> >>
> >
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> >
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