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How to fit a general-linear mixed-effects model with categorical variables?

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Falk Lieder
Falk Lieder on 5 Oct 2015
Edited: Falk Lieder on 6 Oct 2015
Hi,
I am using the function fitglme from the statistics toolbox to fit a mixed-effects model with repeated measurements and categorical predictor variables as follows:
data_nr_acquisitions=table(nr_acquisitions,problem_type,block,subject);
%mixed-effects GLM that allows the effects of the problem type, the offset, and the
%block to vary randomly between subjects.
glme = fitglme(data_nr_acquisitions,'nr_acquisitions ~ problem_type + block + (problem_type| subject) + (block| subject) + (1|subject)');
anova(glme)
The variable problem_type is categorical, but when I run anova it says that problem_type has only one degree of freedom even though it has four possible values. This suggests that Matlab is treating it as a continuous regressors rather than as a categorical variable. Hence, something went wrong.
I tried to instruct fitglme to treat problem_type as a categorical variable with the argument "CategoricalVars" but unlike fitglm the function fitglme does not accept this argument. Can fitglme handle categorical variables and how can I get it to treat a variables as categorical?

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