`In the trivial case where all candidate predictors have one degree of`

`freedom (which is unlikely as some things will be nonlinear or have >`

`2 categories), adding a variable if it increases AIC is the same as`

`adding it if its chi-square exceeds 2. This corresponds to an alpha`

`level of 0.157 for a chi-square with 1 d.f. At least AIC leads`

`people to use a more realistic alpha (small alpha in stepwise`

`regression leads to more bias in the retained regression`

`coefficients). But you still have serious multiplicity problems, and`

`non-replicable models.`

`Things are different if you have a pre-defined group of variables you`

`are thinking of adding. Suppose that this group of 10 variables`

`required 15 d.f. Adding the group if AIC (based on 15 d.f.)`

`increases wouldn't be a bad strategy. This avoids the multiplicities`

`of single-variable "looks".`

Frank Frank E Harrell Jr Professor and Chairman School of Medicine Department of Biostatistics Vanderbilt University On Mon, 9 Aug 2010, Kingsford Jones wrote:

On Mon, Aug 9, 2010 at 10:27 AM, Frank Harrell <f.harr...@vanderbilt.edu> wrote:Note that stepwise variale selection based on AIC has all the problems of stepwise variable selection based on P-values. AIC is just a restatement of the P-Value.I find the above statement very interesting, particularly because there are common misconceptions in the ecological community that AIC is a panacea for model selection problems and the theory behind P-values is deeply flawed. Can you direct me toward a reference for better understanding the relation? best, Kingsford JonesFrank Frank E Harrell Jr Professor and Chairman School of Medicine Department of Biostatistics Vanderbilt University On Mon, 9 Aug 2010, Gabor Grothendieck wrote:On Mon, Aug 9, 2010 at 6:43 AM, Harsh <singhal...@gmail.com> wrote:Hello useRs, I have a problem at hand which I'd think is fairly common amongst groups were R is being adopted for Analytics in place of SAS. Users would like to obtain results for logistic regression in R that they have become accustomed to in SAS. Towards this end, I was able to propose the Design package in R which contains many functions to extract the various metrics that SAS reports. If you have suggestions pertaining to other packages, or sample code that replicates some of the SAS outputs for logistic regression, I would be glad to hear of them. Some of the requirements are: - Stepwise variable selection for logistic regression - Choose base level for factor variables - The Hosmer-Lemeshow statistic - concordant and discordant - Tau C statisticFor stepwise logistic regression using AIC see: library(MASS) ?stepAIC For specifying reference level: ?relevel ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.

______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.