[R-sig-teaching] demonstration of weaknesses in stepwise variable selection

[Jeff Laux]

Although no code is given, it can be inferred from this:
      https://stats.stackexchange.com/a/179945/
Best, Jeff


On 10/2/2018 11:54 AM, R. Mark Sharp wrote:
> 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
> rmsharp using me.com
>
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