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Multivariate problems . . . with 200 resposes variables and 1 explanatory variable

On Wed, 2009-11-25 at 16:55 -0800, ychu066 wrote:
You give very little to go on (please read the posting guide for future
reference), but:

If you want to analyse all the responses at once:

A VGLM might be useful; see the VGAM package on CRAN and the author's
(Thomas Yee) web site for lots of useful information.

A constrained ordination might also be useful. cca() in package vegan,
or capscale() (also in vegan) with a suitable dissimilarity for ordinal
data (see daisy() in package cluster for such dissimilarities).

If you want to model the 200 responses separately (i.e. 200 models) then
polr() in package MASS or lrm() in package rms would be places to start.

HTH

G