I have run a number of hurdle models and these models have the lowest AIC value, within 2 AIC of eachother. I am trying to see if there is a way to visualise model fit for both of these models.

m10 <- mixed_model(Anticipatory ~ Zoo + Born_Transfer, random = ~1|Focal_Name,
                   data = Camels,
                   family = hurdle.lognormal(),
                   n_phis = 1, zi_fixed = ~ Zoo + Born_Transfer, control = list(iter_EM = 0))

m24 <- mixed_model(Anticipatory ~ Zoo + Sex + Born_Transfer, random = ~1|Focal_Name,
                   data = Camels,
                   family = hurdle.lognormal(),
                   n_phis = 1, zi_fixed = ~ Zoo + Sex + Born_Transfer, control = list(iter_EM = 0))

It was suggested to use rootograms, but the pscl package does not support random effects.

Is there an alternative to check model fit?