[R] Checking modeling assumptions in a binomial GLMM

[Ben Bolker]

Ravi Varadhan <ravi.varadhan <at> jhu.edu> writes:

> 
> Dear All,
 
> I am fitting a model for a binary response variable measured
> repeatedly at multiple visits.  I am using the binomial GLMM using
> the glmer() function in lme4 package.  How can I evaluate the model
> assumptions (e.g., residual diagnostics, adequacy of random effects
> distribution) for a binomial GLMM?  Are there any standard checks
> that are commonly done?  Are there any pedagogical examples or data
> sets where model assumptions have been examined for binomial GLMMs?
 
> Any suggestions/guidance is appreciated.
> 
> Thank you,
> Ravi


  This might be better for r-sig-mixed-models at r-project.org.

  Roughly speaking, you want to do one set of diagnostics on
the individual-level residuals similar to those for a binomial GLM 
(which in turn are adaptations of the diagnostics for linear models)
and one on the group-level random effects.  As with GLMs, if your
binomial values are _binary_ then the individual-level diagnostics
will be a bit challenging.  Binomial GLMMs with N>1 will be a bit
easier.

  http://rpubs.com/bbolker/glmmchapter may be helpful, especially the second
("Culcita") example.

  Also http://stats.stackexchange.com/questions/70783/
   how-to-assess-the-fit-of-a-binomial-glmm-fitted-with-lme4-1-0/

(broken URL to make Gmane happy)