"Anova.mlm" would be one way to do model selection.
On Fri, Feb 10, 2012 at 4:29 PM, Fugate, Michael L <fug...@lanl.gov> wrote: > Good Day, > > I fit a multivariate linear regression model with 3 dependent variables and > several predictors using the lm function. I would like to use stepwise > variable selection to produce a set of candidate models. However, when I > pass the fitted lm object to step() I get the following error: > > Error from R: > Error in drop1.mlm(fit, scope$drop, scale = scale, trace = trace, k = k, : > no 'drop1' method for "mlm" models > > My dependent data is in the matrix ymat where ymat is 35 rows by 3 columns. > The predictors are in X where X is 35 by 6 > > The steps I used were: > m.fit <- lm(ymat ~ ., data=X) > m.step <- step(m.fit) > > If variable selection is not possible with step() is there another package > that will perform variable selection in a multivariate setting? > > System information: > platform x86_64-apple-darwin9.8.0 > arch x86_64 > os darwin9.8.0 > system x86_64, darwin9.8.0 > status > major 2 > minor 13.1 > year 2011 > month 07 > day 08 > svn rev 56322 > language R > version.string R version 2.13.1 (2011-07-08) > > Thanks in advance. > > ______________________________________________ > 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.
