So, I'm performing generalized linear mixed models with a poisson distribution and an offset. When looking at the Cook's distance, I found gigantic values (above 3000). When removing the concerned observation, the model fail to converge. Note that all independent variables have been scaled.
I would like to include my data for an example, but I don't know how to do that here. If someone can point me how-to, I will improve my question
1- Am I doing something wrong here? Like, using a function I'm not supposed to use.
2- What does it mean?
3- What should I do with this outlier?
The model, that converges:
mod1 <- glmer(C.cent ~ richness.s + Densit.s + richness.s:Densit.s + PIB.s + richness.s:PIB.s + offset(log(Dispo.cent)) + (1|Transect), family=poisson, data=data)
Calculating cook's distance:
imod1 <- influence(mod1, obs = TRUE) plot(cooks.distance.estex(imod1)) identify(cooks.distance.estex(imod1)) #Outlier : observation 85
Removing the outlier, the model doesn't converge:
temp <- data[c(1:84, 86:103),]
mod2 <- glmer(C.cent ~ richness.s + Densit.s + richness.s:Densit.s + PIB.s + richness.s:PIB.s + offset(log(Dispo.cent)) + (1|Transect), family=poisson, data=temp))