I'm trying to detect correlations between my variables, and I should be able to find this by inverting the correlation matrix and looking at the diagonal values, which are the VIF values. I can't do this because my matrix is singular, which I think means there is correlation between two or more of my variables.
However, I've been trying to remove some, and the only time the matrix becomes nonsingular is when I remove the dummy variables that I have.
From this post it seems that a high VIF is likely to occur when you have dummy variables. When I look at the correlations there are correlations of around 0.5 between two levels of the same original variable. So what exactly do you do in this situation? I could drop all the variables that I have dummy coded but I don't think that would be a good model anymore. Do I simply have to find a different diagnostic for multicollinearity, because I won't be able to invert the matrix?