### Re: [R] Logistic Regression in R (SAS -like output)

Frank E Harrell Jr Professor and ChairmanSchool of Medicine Department of Biostatistics Vanderbilt University On Mon, 9 Aug 2010, Harsh 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. The replacement for Design, rms, has some new indexes. 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 an invalid procedure - Choose base level for factor variables not relevant - get what you need from predicted values and differences in predicted values or contrasts - this automatically takes care of reference cells - The Hosmer-Lemeshow statistic obsolete: low power and sensitive to choice of binning - concordant and discordant see Hmisc's rcorr.cens - Tau C statistic Those are two different statistics. tau and C are obtained by lrm in rms/Design. Frank Thank you for your suggestions. Regards, Harsh Singhal __ 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.

### Re: [R] Logistic Regression in R (SAS -like output)

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 statistic For 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.

### Re: [R] Logistic Regression in R (SAS -like output)

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. Frank Frank E Harrell Jr Professor and ChairmanSchool 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 statistic For 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.

### Re: [R] Logistic Regression in R (SAS -like output)

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 ChairmanSchool 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 Jones Frank 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 statistic For 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.