I'm using linear mixed model with the lme4 package in R to analyze EEG amplitude in two different subject groups (control & experiment).
All subjects were instructed to view images of two different categories (car vs human). While category 1 solely comprises of whole cars, category 2 can then be divided into three sub-categories (head, trunk, extremities).
My dataset looks like this:
Now I am interested in the effect of group, category, as well as sub-category on EEG amplitude. I am also interested in the interaction between group and category, and group and sub-category.
Hence, I would like to define category and sub-category as fixed effects. My current model looks like this:
AMP.model = lmer(amplitude ~ group * (category + subcategory) + (1|patient), data=AMP)
But then i receive the following warning:
fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
I believe this is due to the assumption in linear mixed models that fixed effects should not be correlated / nested. But how else would I get the information that I am interested in?
Please note that it is not an option to remove the categories and only check for sub-categories, as my main hypothesis is based on the effects of categories on EEG amplitude.