When running a GLMM in R with
link=identity, it's easy enough to test whether normality and homoscedasticity of the residuals have been achieved (
qqnorm plots and shapiro-wilks tests for normality and residuals vs. fitted plots for homoscedasticity). But what process should I use to test assumptions have been met when using a non-gaussian family and a non-identity link?
I'm mostly interested in how to test to see if the residuals are consistent with an inverse gaussian family and log-link, but how to assess for a gamma distribution might also be useful.
Here is a histogram of my GLMM residuals when I used an inverse gaussian family and log-link in case it helps at all: http://i.imgur.com/PlK5Q33.png
From what I've found, it sounds like
qqPlot from the car package might be useful, but I'm not sure how to go about choosing shape parameters and such like. As well, that only accounts for the distribution I specified..not the link function.
Thanks in advance!