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.harr...@vanderbilt.edu wrote:
Given
'On Tue, Dec 7, 2010 at 5:19 AM, mxkuhn mxk...@gmail.com wrote:
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)
Try this:
On Tue, Dec 7, 2010 at 7:54 AM, Gabor Grothendieck
ggrothendi...@gmail.com wrote:
'On Tue, Dec 7, 2010 at 5:19 AM, mxkuhn mxk...@gmail.com wrote:
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
I'd like to make a less than full rank design using dummy variables
for factors. Here is some example data:
when - data.frame(time = c(afternoon, night, afternoon,
morning, morning, morning,
morning, afternoon, afternoon),
Data Center
Intermountain Healthcare
greg.s...@imail.org
801.408.8111
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
project.org] On Behalf Of Max Kuhn
Sent: Monday, December 06, 2010 8:35 AM
To: r-help@r-project.org
Subject: [R] less than full rank
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
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