[R-sig-ME] Checking modeling assumptions in a binomial GLMM

[Francisco Rodriguez Sanchez]

Dear Ravi,

I wonder if you received some answer off-list. There are some functions 
for simple logistic regression that I think can be applied to binomial 
GLMMs as well, such as binnedplot in arm package 
(http://www.rdocumentation.org/packages/arm/functions/binnedplot) to 
check residuals, or plotCalibration in PredictABEL 
(http://www.rdocumentation.org/packages/PredictABEL/functions/plotCalibration) 
to check goodness of fit.

I tried to put together these tests in a single function some time ago, 
check here: 
https://github.com/Pakillo/pacotools/blob/master/R/checkLogReg.R.

Hope this helps,

Paco


El 14/07/2014 16:19, Ravi Varadhan escribió:
> Dear All,
>
> I am fitting a model for a binary response variable measured repeatedly at multiple visits.  I am fitting the binomial GLMM using the glmer() function in lme4 package.  How can I evaluate the model assumptions (e.g., residual diagnostics, covariate form, adequacy of random effects distribution) for a binomial GLMM?  Could you please give me some suggestions on how to do this?  Are there any pedagogical examples or data sets where these issues have been demonstrated for binomial GLMMs?
>
> I appreciate your help very much.
>
> Thank you,
> Ravi
>
>
> Ravi Varadhan, Ph.D. (Environmental Eng.), Ph.D. (Biostatistics)
> Associate Professor,
> Division of Geriatric Medicine & Gerontology
> School of Medicine,
> Johns Hopkins University
> Ph: 410-502-2619
> Email: ravi.varadhan at jhu.edu<mailto:ravi.varadhan at jhu.edu>
> http://www.jhsph.edu/research/centers-and-institutes/johns-hopkins-center-on-aging-and-health/people/Faculty_personal_Pages/Varadhan.html
>
>
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>
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-- 
Dr Francisco Rodriguez-Sanchez
Integrative Ecology Group
Estacion Biologica de Doñana - CSIC
Avda. Americo Vespucio s/n
41092 Sevilla (Spain)
http://sites.google.com/site/rodriguezsanchezf