[R-sig-ME] GLMM & lack of linearity on the logit

[Gavin Simpson]

On Mon, 2010-07-05 at 10:45 +1000, Simon Blomberg wrote:
> Hi Luciano,
> 
> In general, categorization or "binning" is a bad thing to do. You are 
> throwing away information in the process, and the size and number of 
> bins must be subjective at some level. If there is important 
> nonlinearity in egg volume, you could consider a Generalized Additive 
> Mixed Model (GAMM) with a smoothing term for egg volume. I recommend 
> package mgcv.

If you use mgcv to fit the GAMM you are using MASS::glmmPQL -> lme,
which will use PQL for the fitting. There are two packages on CRAN that
use lme4 as the underlying fitting code whilst allowing smooth functions
of covariates as per a GAM; amer and gamm4. The latter is by Simon Wood
(author of mgcv) though Fabian's amer package was the first to use the
trick of fitting via lmer. Neither are as full-featured as mgcv::gamm
and the types of smooths allowed is restricted, but amer and gamm4
should be more numerically robust than gamm.

G

> 
> Cheers,
> 
> Simon.
> 
> On 05/07/10 10:34, Luciano La Sala wrote:
> > Dear R-people,
> >
> > I have just received from reviewers of a manuscript some harsh comments on
> > the statistical procedures. I'm studying risk factors of mortality at the
> > nest level among Olrog's Gull nest mates, which is why I used mixed models
> > with random intercepts (Nest ID). The outcome of interest if "Death"
> > (yes/no) and one of my explanatory variables is "Egg Volume" (continuous).
> > Since violation of linearity on the logit was evident I created 4 categories
> > using the quartiles of the distribution and modeled them as dummies.
> >
> > However, one reviewer stated: "It is unclear why you used volume of eggs as
> > a factor (i.e. categorized variable) in the analyses. Incorporating this
> > predictor as a continuous variable, as was originally measured, would make
> > analysis more informative. You stated that you made so "to relax the
> > linearity assumption". GLMM are sufficiently robust to accept a continuous
> > variable into a categorized model that, with the correct link function and
> > the variable transformation, would support well the linearity assumption."
> >
> > That said, I wonder if (1) categorization is such a bad thing on the one
> > hand, and (2) lack of linearity on the logit scale can be handled well by
> > GLMM.
> >
> > In my case, adding quadratic and cubic terms after assessment of the shape
> > of the x-y relationship did not improve the fit, so I decided to use dummies
> > and thus relax the linearity assumption.
> >
> > Thank you very much in advance.
> >
> > Luciano
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >    
> 

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