I have a set of count data that seems to fit "Poisson" = not overdispersed, alpha = 0.
The problem is, I get different results using
glmer. Any help explaining the difference would be appreciated:
Treatment is a factor with 6 levels.
Trial is a random intercept factor with 3 levels.
Glmer.POI <- glmer( Y ~ Treatment + offset(log(Total)) + (1|Trial), family=poisson)) Gamlss.PO <- gamlss(Y ~ Treatment + offset(log(Total)), random=~1|Trial, family=PO) # Glmer result: AIC = 284. deviance = 270 # Gamlss result: AIC = 388. deviance = 376
I get different coefficients and different p-values with the 2 models. When I look at the diagnostic plots, I see residuals that increase with fitted values in the gamlss model "heteroscedasticity", but not with the glmer.
My intuition tells me this is related to how random effects are handled, but how?
I think I had similar problems with another dataset using negative binomial. I pulled the question from this site to concentrate on this one: If gamlss and glmer behaviour with random effects explains both problems, Great! But I'd like to know how a biologist (non statistician) "poor soul" like me can understand how to use these tools and choose the right one! ;-)