[R] Question about validating predicted probabilities

[Frank E Harrell Jr]

A parametric version is:

require(Design)
dd <- datadist(predprob); options(datadist='dd')
f <- lrm(event ~ rcs(qlogis(predprob), 3))
plot(f, predprob=NA, fun=plogis)

Frank


Noah Silverman wrote:
> Hello,
> 
> Frank was nice enough to point me to the val.prob function of the Design 
> library.
> 
> 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" 
> line!!
> 
> 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?
> 
> Thanks!
> 
> -N
> 
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-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University