"Regression Modeling Strategies" published by Springer.
SR Millis
Scheltema, Karen wrote:
I know about the perils of stepwise, and I agree with you that it is a less than desirable procedure. This researcher, however, is not as convinced as I am about not doing stepwise. Sigh. He has more variables than would comfortably fit a 5-1 case to variable ratio for a forced entry regression, which is why he was hoping stepwise would help him narrow his model. Any suggestions I can give him, short of telling him to scrap everything?
Karen Scheltema, M.A, M.S. Senior Statistician HealthEast Research and Education Department, Midway Campus 1700 University Ave W St. Paul, MN 55104 Ph: (651) 232-5212 fax: (651) 641-0683 mailto:[EMAIL PROTECTED]
-----Original Message----- From: Paul R Swank [mailto:[EMAIL PROTECTED] Sent: Wednesday, May 28, 2003 8:26 AM To: Scheltema, Karen; Ed Stat (E-mail) Subject: RE: VIF for dichotmous variable
This is another good illustration why one should not use automatic procedures for research. The problem of interactions in stepwise procedures is that the compute doesn't know it is an interaction and that you can't drop the main effects while the interaction is strill in the model. If the interaction is significant then the main ewffects that make up the interaction must stay in the model. By the way, there is backward selection, forward selection, and stepwise selection (plus several other more esoteric procedures) but I, for one, have never heard of backwward stepwise.
Paul R. Swank, Ph.D. Professor, Developmental Pediatrics Medical School UT Health Science Center at Houston
-----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Behalf Of Scheltema, Karen Sent: Tuesday, May 27, 2003 4:08 PM To: [EMAIL PROTECTED] Subject: VIF for dichotmous variable
I have a colleague who has run a backwards stepwise linear regression. A
couple of the variables are problematic. One is a dichotomous variable that
indicates trying to get pregnant (y/n) and the other is a continuous
variable that is a scale score. It is believed that the two interact with
each other. In the initial run, the scale was centered, but the dichotomous
variable was not. This yielded high VIFs (>100) in the initial regression
equation, but the scale dropped out by the latter stages of the stepwise
regression. I suggested centering the dichotomous variable, as well, but
this yielded VIFs>300 in the initial steps. Since this scale variables
drops out later, can I just ignore this? Or, can someone suggest another
solution to try?
Karen Scheltema, M.A, M.S. Senior Statistician HealthEast Research and Education Department, Midway Campus 1700 University Ave W St. Paul, MN 55104 Ph: (651) 232-5212 fax: (651) 641-0683 mailto:[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]>
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