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.

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?

3) Your Design library rocks.  Thank you so much for making it available 
to the R community!!

-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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>> PLEASE do read the posting guide 
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>>
>
>

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