I have a simple data set with some very frustrating distributions. We’re simply comparing the morphology of two groups of grasshoppers. Using a GLMM kept giving us impossible to interpret results that appeared to be type I errors so we opted for some simple non-parametric tests instead.
In short the reviewers have accepted the paper but don’t like the simple stats and would like us to further justify why a model isn’t possible (I’ve copied a quite below). I’m a bit lost and not sure how to demonstrate convincingly why distribution problems and collinearity are proving difficult to deal with.
Is anyone able to help out and have a chat about what information I could present to demonstrate in the paper that the model wasn’t working? OR maybe I am missing something entirely and a model will work after all…
P.S. Apologies if this is confusing, its not my data set so I’m a bit lost too!
“Firstly, in a normal procedure of exploratory analysis, as the authors know, high levels of co-linearity between explanatory variables in a model can be avoided by manually checking correlation between all the pairs of the variables and omitting one variable of highly correlated pair. If the high levels of co-linearity could not be excluded even after this procedure, please explain so. Secondly, did zero-inflation of ‘explanatory’ variable in the GLM model really result in apparent type I error? Of course, zero-inflation in ‘response’ variable often causes this type of problem, but this is not the case. Please check this again”