I am attempting to analyze some data that includes 10 repeated measurements on 10 different samples. This would normally require using mixed models, and I have attempted to model these using
lme in nlme and using
glmer in lme4. The problem is that for the majority of these samples, the response curve is a flat line (i.e., slope is zero) due to several samples having all zero values. This means that the random effects are not going to be normally distributed, which is an assumption of mixed models.
I have considered using 0 inflated ZINB models. My understanding is these assume that some of the zeros are false zeros. In my case, all of the zeros are real zeros. Does this negate using ZINB models? Even if it doesn't, I'm not sure this takes care of the normal random effects issue. I tried using glmmadmb to model this data using
family = nbinom and
zeroInflated = TRUE, but I don't know if this is appropriate given the data. Any input would be greatly appreciated.