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-mod...@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 N1 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)
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
