Noah Silverman wrote:
Thanks Frank,
Two quick questions:
1) I see you calculating datadist, but then not using it in the
subsequent entries. Is that for a different application.
That's just to set default plotting limits.
2) I'm less concerned with plotting than the values that were plotted.
As mentioned in my original message, The line plotted from the fitted
logistic looked great. I want those values. Perhaps all I need is the
"lrm" line of your example?
The fitted logistic model in your plot is forced to be linear in the
logit (log odds of pred. prob.). The spline function relaxes that
making if halfway between the linear one and the loess one.
3) Your Design library rocks. Thank you so much for making it available
to the R community!!
Thanks
Frank
-N
On 8/21/09 3:00 PM, Frank E Harrell Jr wrote:
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
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