less than full rank contrast methods
Greg and Frank, Thanks for the replies. I didn't express myself very well; I'm not interest in the model fitting aspect. I'd just like to get the full set of dummy variables (optimally from model.matrix) Max
On Dec 6, 2010, at 10:29 PM, Frank Harrell <f.harrell at vanderbilt.edu> wrote:
Given a non-singular fit, the contrast function in the rms package will allow you to request multi-dimensional contrasts some of which are redundant. These singular contrasts are automatically ignored. One use for this is to test for differences in longitudinal trends between two of three treatment groups, where the time trend is a multiple degree of freedom parameterization such as cubic splines. You don't have to stop and think about how many time points to test; just test as many as you'd like and get the right degrees of freedom according to the number of spline terms (main effects + interactions). Frank ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/less-than-full-rank-contrast-methods-tp3074688p3075771.html Sent from the R help mailing list archive at Nabble.com.
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