SO (stats.stackexchange.com) is the better list for methodological issues like this.
Cheers, Bert Bert Gunter Genentech Nonclinical Biostatistics (650) 467-7374 "Data is not information. Information is not knowledge. And knowledge is certainly not wisdom." H. Gilbert Welch On Sun, Jan 26, 2014 at 1:33 PM, Professor George F.Hart <gh...@lsu.edu> wrote: > 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. ______________________________________________ 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.
