[R] Question about validating predicted probabilities
Frank E Harrell Jr
f.harrell at vanderbilt.edu
Sat Aug 22 00:00:17 CEST 2009
A parametric version is:
dd <- datadist(predprob); options(datadist='dd')
f <- lrm(event ~ rcs(qlogis(predprob), 3))
plot(f, predprob=NA, fun=plogis)
Noah Silverman wrote:
> Frank was nice enough to point me to the val.prob function of the Design
> It creates a beautiful graph that really helps me visualize how well my
> model is predicting probabilities.
> By default, there are two lines on the graph
> 1) fitted logistic calibration curve
> 2) nonparametric fit using lowess
> Right now, the nonparametric line doesn't look very good.
> The "fitted logistic" line looks great. It is right next to the "ideal"
> If I am understanding the graph correctly, whatever transformation the
> val.prob is doing to my predicted probability is making it really accurate.
> Is there some standard function in R that will let me do the same
> transformation? (I guess the long way around would be to tear into the
> actual val.prob function and try to reverse engineer what he's doing.
> But there must be something easier.)
> Anybody have any suggestions?
> R-help at r-project.org mailing list
> PLEASE do read the posting guide
> and provide commented, minimal, self-contained, reproducible code.
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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