I am working on a problem in which I have derived a set of D formulae relating a different dependent variable to a grouping of independent variables.
D1 = intercept + ax1 + bx2 + bx3 + bx4 D2 = intercept + ex2 + fx7 + gx8 D3= intercept + hx1 + ix3 + jx7 etc to ... D8. I have 3 categorical variables P, Q and A [which are actually hierarchical with A within Q with P, each containing a different number of classes] – I want to look at each of the categorical variables as a separate issue clustering each of the D formulae into classes, so I can say something about how the D's vary / interact across classes. Intuitively this seems to be a discriminant function problem because the classes are already known. However, a PCA or FA might be necessary – and then do a DFA on the clusters. Either way I am not sure how to set it up or even if I can interpret it to make sense. Alternatively, I might be climbing up/down the wrong tree [pun intended]. Other methods might be better. Help! George F. Hart I'm sending this to a number of statistics groups so apologize if you get this note more than once. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
