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Hello All, I have a dataset with a continuous response variable and an ordered factor predictor. I am very interested in using the package OrdFacReg to run my analysis, but I am having a difficult time deciphering the code and...

Hi Chris, My understanding is that comparing GLM to GLMM right now is not possible via multimodel model inference (MMI) because the number of degrees of freedom for random effects are estimated to be equal to fixed effects and this...

Hi Mauro, I agree with both Thierry and Luciano, considering your nested design the random effects lie at the level of the individual. My only concern is that given the coding below, only the intercept varies and the slopes are...

Hi Mike, Sounds as though you are debating about model comparison techniques. I would suggest using multimodel inference if you do not have any one model that is strongly supported by AIC values. Burnham & Anderson published a book on the...

Hi Dennis, Thank you for your response. No, NB is not a matrix, and I have no covariates. Here's a very small sample of the data: effect NB -0.003200 1 -0.120800 3 -0.003200 2 -7.690000...

Hello All, Instead of using step-wise selection, I would suggest instead using multimodel inference (Burnham & Anderson). The technique avoids having to choose one "right" model and, in my opinion, is a more accurate method than traditional step-wise procedures...

Hi Nathan, I only quickly read your email, but I believe you were asking about step-wise regression. I would strongly suggest that instead you consider using multimodel inference (Burnham & Anderson). If you want I can suggest for you a...