|Date: ||Mon, 28 Sep 1998 15:26:34 -0500|
|Reply-To: ||"Gary D. & Franchesca D. Zenitsky" <gdzenitsky@MOCHA.MEMPHIS.EDU>|
|Sender: ||"SAS(r) Discussion" <SAS-L@UGA.CC.UGA.EDU>|
|From: ||"Gary D. & Franchesca D. Zenitsky" <gdzenitsky@MOCHA.MEMPHIS.EDU>|
|Subject: ||AIC in stepwise logistic?|
|Content-type: ||text/plain; charset="us-ascii"|
I'm new to the list, so please excuse if the following question's topic is
too simplistic or not appropriate for this forum:
I'm running PROC LOGISTIC using stepwise model selection with a binary
response. I'm comparing 4-5 models, with any where from 3 to 5 explanatory
variables selected from 7, with 140 observations. The procedure produced
these models from runs on the original data as well as on bootstrapped
data, all using SLE/SLR of 0.25. To compare the models, I've been looking
at the c-index and the H-L lack-of-fit test.
Here's my question: if low values of AIC indicate a more fit model, then
why are the intercept- only values for AIC much smaller than those with
covariates added in? Also, the model with the least lack-of-fit (H-L
statistic) has the largest AIC value. I'm left wondering what it all means
because the model I presumed to be "best" is the one with the largest AIC.
I understand that the number of obs and variables has a role here, and that
there may be a cut-off point that's above the minimum AIC. The data is of
the field ecology kind, and therefore, whether or not the "true" model
(?-var) is contained within the global model (7-var) is unknown.
Oh yes, did I mention that I'm desperate for some expert insight on this
matter. I do thank everyone in advance for any responses.
Gary D Zenitsky
Department of Biology
University of Memphis
Memphis, TN 38152
Phone wk/hm: 901-678-3322/386-6565