ychu066
2009-Nov-26 00:55 UTC
[R] Multivariate problems . . . with 200 resposes variables and 1 explanatory variable
How should I analysis it in R ???? all the resposes variables are ordinal from 0 to 10. and the explanatory variable is a factor ... -- View this message in context: http://old.nabble.com/Multivariate-problems-.-.-.-with-200-resposes-variables-and-1-explanatory-variable-tp26522912p26522912.html Sent from the R help mailing list archive at Nabble.com.
Jason Morgan
2009-Nov-26 03:25 UTC
[R] Multivariate problems . . . with 200 resposes variables and 1 explanatory variable
Please see the posting guide here: http://www.r-project.org/posting-guide.html In short, it would be helpful if you provided more information on your data and what the goal of your analysis is. However, to get you started, see the polr() function in the MASS package. Depending on your goal/data, that may help. ~Jason On 2009.11.25 16:55:13, ychu066 wrote:> > How should I analysis it in R ???? all the resposes variables are ordinal > from 0 to 10. and the explanatory variable is a factor ... > -- > View this message in context: http://old.nabble.com/Multivariate-problems-.-.-.-with-200-resposes-variables-and-1-explanatory-variable-tp26522912p26522912.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help at 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.-- Jason W. Morgan Graduate Student Department of Political Science *The Ohio State University* 154 North Oval Mall Columbus, Ohio 43210
Gavin Simpson
2009-Nov-26 07:38 UTC
[R] Multivariate problems . . . with 200 resposes variables and 1 explanatory variable
On Wed, 2009-11-25 at 16:55 -0800, ychu066 wrote:> How should I analysis it in R ???? all the resposes variables are ordinal > from 0 to 10. and the explanatory variable is a factor ...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 -- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Dr. Gavin Simpson [t] +44 (0)20 7679 0522 ECRC, UCL Geography, [f] +44 (0)20 7679 0565 Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ UK. WC1E 6BT. [w] http://www.freshwaters.org.uk %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%