Hello, I am trying to run mixed effect zero-inflated models in order to investigate spatial variability in density of a intertidal barnacle and effect of environmental variables. First, I am trying to determinate the best random model. I have 3 scales of variability: *region* (2 levels), *sub-regions* (3 levels nested in each region = 6) and *locations* (number of levels *unbalanced* nested in each sub-region and region, total = 62). I am using negative binomial family (nbinom1: best fit than other possibilities). After selecting such model, I intend to compare it with most parsimonious mixed model considering random effects plus the effect of a specific predictor. For example: Best random model: density ~ subregion + location Best mixed model: density ~ temperature + subregion + location My doubts are: 1) Following recommended approaches for regular GLMM's, should I use REML instead of ML for estimation of parameters? 2) For making models comparable, how should I set zero-inflation component? I am using ziformula = 1. Is that the best approach? When I try to consider all terms in zero-inflation component (ziformula = ~.) it usually leads to errors. Overparameterisation I guess. 3) Once I have my best random and mixed models, should I refit them using ML method and then compare them (as recommended for regular GLMM's)? 4) (Finally) How to validate model? I am trying to use DHARMa package for checking simulated residuals vs fitted values, but it doesn't work well for models with strong random effects (my case). Thank you so very much. My best, Andre.
Visiting PhD student School of Ocean Sciences Bangor University Menai Bridge, Anglesey, UK [[alternative HTML version deleted]]