The functions bsnVaryNvar() and bestsetNoise() in the DAAG package have been designed to highlight the opportunities that conventional variable selection methods offer for generating “significant” effects from data that is pure noise. There is an accompanying vignette simulate-varselect.
It is a sad commentary on the limited extent to which an informed and incisive critical appraisal is commonly applied to results tossed out by packaged software that standard forms of backward and forward regression variable selection, and best subsets regression, continue to be used as the basis for published work. John Maindonald email: [email protected]<mailto:[email protected]> On 3/10/2018, at 23:00, [email protected]<mailto:[email protected]> wrote: Message: 1 Date: Tue, 02 Oct 2018 10:54:36 -0500 From: "R. Mark Sharp" <[email protected]<mailto:[email protected]>> To: [email protected]<mailto:[email protected]> Subject: [R-sig-teaching] demonstration of weaknesses in stepwise variable selection Message-ID: <[email protected]<mailto:[email protected]>> Content-Type: text/plain; charset="us-ascii" I am developing a short presentation for people with applied statistical backgrounds who have used backward stepwise variable selection where they remove variables based on small coefficient values, coefficient P values > 0.05, and large variances. I am wanting to provide some demonstration code in R that highlights some of the weakness as described by Frank Harrell (citation below). Of particular interest are (1) failure to include informative predictor variables (categorical and continuous) and (2) lowered standard errors for the coefficients in the final model. I have code to demonstrate inclusion of too many false predictors. I expect this code is available, but I have not found it. Guidance would be appreciated. Mark P.S. I have started a public github package at https://github.com/rmsharp/stepwiser I has very little in it thus far. Frank E. Harrell. Regression Modeling Strategies with applications to linear models, logistic regression, and survival analysis, Springer Series in Statistics. Springer-Verlag. 2015. R. Mark Sharp, Ph.D. Data Scientist and Biomedical Statistical Consultant 7526 Meadow Green St. San Antonio, TX 78251 mobile: 210-218-2868 [email protected]<mailto:[email protected]> [[alternative HTML version deleted]] _______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-teaching
