I have a dataset with thousands of observations pre-assigned to 18 groups and with measures for 8 different variables. I am using canonical discriminant analysis to see how separable my 18 groups are. What I am actually most interested in is which individual variable separates the groups most (and least).
I have tried running canonical discriminant analysis in R using the ldm() function from the MASS library.
mydata.lda <- lda(group ~ x1 + x2 + x3 .... + x8, data=mydata)
If I understand correctly, the output has coefficients of linear discriminant which indicates how strongly each variable is associated with each individual discriminant function, and I could standardize the coefficients to help interpret the meanings of the resultant discriminant functions.
I think what I want however is the partial F-square of each individual variable, or the relative ability of each variable to separate groups across all discriminant functions, not one at a time. In SPSS, the discriminant analysis function allows one to ask for "univariate ANOVAs" which seem to produce what I want: a table showing the Wilks' Lambda statistic and F statistics for each of my 8 variables. How would I get this kind of output in R? Do I need to run a (M)ANOVA based on the output of my lda()?