Hmm, it LOOKS like mvpart may be along the lines of what I want, but is
mvpart a nominal classification tree, or can it handle multiple,
continuous response variables as well?
--j
Gene Leynes wrote:
This sounds very similar to what I've been working on, but I'm not
sure without an example.
My solution has been to use an optimization that normalizes inside the
objective function. The betas that are provided by optim are not
normalized, however since they were normalized inside the objective
function, normalizing them after the fact mirrors the internal
workings of the objective function.
see this example:
http://markmail.org/message/ze5237m6gbgvvvyf
Still, after looking at several statistical packages, and considering
the thoughtful responses from my post, I think that there must be a
better way using existing models, so I've been looking at other
packages / models.
On Tue, Apr 7, 2009 at 6:10 PM, Jonathan Greenberg
<greenb...@ucdavis.edu <mailto:greenb...@ucdavis.edu>> wrote:
R'ers:
I was hoping I could get some direction on this. I have a
dataset of the form:
Y1,Y2,...,YM = f(X1,X2,...,XN), where N is >>> M
The response data (Y1,Y2,...,YM) is frequency data, such that the
sum of all Yi = 1.0. Both Xj and Yi are continuous variables.
I'm trying to figure out the best approach(es) to solving for the
model f() -- any ideas? I could solve each Y one at a time, but
the lack of constraint worries me, and I'm pretty sure that
normalizing the data afterwards to sum to 1.0 is not going to work
out properly. Thoughts? I've never worked with multiple response
statistics before, so I'm mostly trying to get some pointers on
where to begin investigating...
--j
--
Jonathan A. Greenberg, PhD
Postdoctoral Scholar
Center for Spatial Technologies and Remote Sensing (CSTARS)
University of California, Davis
One Shields Avenue
The Barn, Room 250N
Davis, CA 95616
Cell: 415-794-5043
AIM: jgrn307, MSN: jgrn...@hotmail.com
<mailto:jgrn...@hotmail.com>, Gchat: jgrn307
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