Ramon Diaz-Uriarte wrote:
Dear All,
For logistic regression models: is it possible to use validate (rms
package) to compute bias-corrected AUC, but have variable selection
with AIC use step (or stepAIC, from MASS), instead of fastbw?
More details:
I've been using the validate function (in the rms package, by Frank
Harrell) to obtain, among other things, bootstrap bias-corrected
estimates of the AUC, when variable selection is carried out (using
AIC as criterion). validate calls predab.resample, which in turn calls
fastbw (from the Design package, by Harrell). fastbw " Performs a
slightly inefficient but numerically stable version of fast backward
elimination on factors, using a method based on Lawless and Singhal
(1978). This method uses the fitted complete model (...)". However, I
am finding that the models returned by fastbw are much smaller than
those returned by stepAIC or step (a simple example is shown below),
probably because of the approximation and using the complete model.
I'd like to use step instead of fastbw. I think this can be done by
hacking predab.resample in a couple of places but I am wondering if
this is a bad idea (why?) or if I am reinventing the wheel.
Best,
R.
P.S. Simple example of fastbw compared to step:
library(MASS) ## for stepAIC and bwt data
example(birthwt)
library(rms)
bwt.glm <- glm(low ~ ., family = binomial, data = bwt)
bwt.lrm <- lrm(low ~ ., data = bwt)
step(bwt.glm)
## same as stepAIC(bwt.glm)
fastbw(bwt.lrm)
Hi Ramon,
By default fastbw uses type='residual' to compute test statistics on all
deleted variables combined. Use type='individual' to get the behavior
in step. In your example fastbw(..., type='ind') gives the same model
as step() and comes surprisingly close to estimating the MLEs without
refitting. Of course you refit the reduced model to get MLEs. Both
true and approximate MLEs are biased by the variable selection so
beware. type= can be passed from calibrate or validate to fastbw.
Note that none of the statistics computed by step or fastbw were
designed to be used with more than two completely pre-specified models.
Variable selection is hazardous both to inference and to prediction.
There is no free lunch; we are torturing data to confess its own sins.
Frank
--
Frank E Harrell Jr Professor and Chairman School of Medicine
Department of Biostatistics Vanderbilt University
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