[SOLVED] ggpredict : confidence interval for negative binomial models

ggpredict : confidence interval for negative binomial models

I used the following code to model count data :

ModActi<-glmmTMB(Median ~ H_veg + D_veg + Landscape + JulianDay + 
    H_veg:D_veg + (1 | Site), 
    data=MyDataActi, family=nbinom2)

I then used the ggpredict function of the ggeffects package to plot the predicted values of my model for the categorical variable "Landscape":

pr1 <- ggpredict(ModActi, "Landscape")
plot(pr1)

I obtain this Graph.

As you can see, lower confidence intervals are negative, as if the function would calculate them for a normal distribution.

In the help menu of ggpredict, it is not clear to me if there is a way to calculate confidence intervals for a negative binomial distribution (as stated in the model)?

If I use glmer in poisson, the confidence intervals are correct.

Solution

This was because glmmTMB only returned predictions on the response scale and these were not back transformed. Now glmmTMB was update on CRAN and I also revised ggeffects. You can try out the current dev-version at https://github.com/strengejacke/ggeffects, which now properly computes the CI (after updating glmmTMB to version 0.2.1).