demonstration of weaknesses in stepwise variable selection

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 at me.com

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 at me.com

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 at me.com