Stepwise regression using p-values to drop variables with nonsignificant p-values

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I want to perform a stepwise linear Regression using p-values as a selection criterion, e.g.: at each step dropping variables that have the highest i.e. the most insignificant p-values, stopping when all values are significant defined by some threshold alpha.

I am totally aware that I should use the AIC (e.g. command step or stepAIC) or some other criterion instead, but my boss has no grasp of statistics and insist on using p-values.

If necessary, I could program my own routine, but I am wondering if there is an already implemented version of this.

p-value regression statistics r
6 Answers

As mentioned by Gavin Simpson the function fastbw from rms package can be used to select variables using the p-value. Bellow is an example using the example given by George Dontas. Use the option rule='p' to select p-value criteria.

require(rms)
model1 <- ols(Ozone ~ Temp * Wind * Solar.R, data=airquality)
model2 <- fastbw(fit=model1, rule="p", sls=0.05)
model2
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