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
[email protected]
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