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.

Cheers! Gary D Zenitsky Department of Biology University of Memphis Memphis, TN 38152 Phone wk/hm: 901-678-3322/386-6565 Fax: 901-678-4746 E-mail: gdzenitsky@cc.memphis.edu