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Can you use GLM with categorical response variable?
I want to know whether I can use glm () with a categorical response variable, and if yes how exactly. I have a response variable with three unordered levels, and both categorical and continuous predictors. Here is an example dataset with just continuous predictors: When I compare the output to that of MCMCglmm ():
Can a multinominal model be used in GLM?
Also, the glm families does not include a multinominal model, and I’m not sure whether the binominal model (as I did above) can be used, despite glm () above not throwing an error. Any help is very welcome. PS In reality, I need a mixed model, which supposedly can be fitted by glmmPQL () in the MASS library, which depends om glm ().
What to do if the dependent variable is categorical in R?
So what I would do is recode your dependent variable to be dichotomous: Keep in mind, though, that you will want to likely include random slopes for group, tone, and focus (or at least test to see if they are significant).
What should the code look like for GLMM?
Keep in mind, though, that you will want to likely include random slopes for group, tone, and focus (or at least test to see if they are significant). The code would look something like this, but only if all of the three predictors are at the lowest level in the multilevel structure of the data:
Are there extensions to beta and Dirichlet regression?
Extensions to beta regression, such as models for variable dispersion, zero-one augmented data and mixed effects designs have been developed and are reviewed and applied to case studies. Finally, we briefly discuss some issues regarding model fitting, inference, and reporting that are particularly relevant to beta and Dirichlet regression.
Can a mixed model be fitted by glmmpql?
PS In reality, I need a mixed model, which supposedly can be fitted by glmmPQL () in the MASS library, which depends om glm (). Hence the question about glm () only.