[R] ROC curves using R
Frank E Harrell Jr
fharrell at virginia.edu
Fri Feb 1 22:28:22 CET 2002
For the purposes for which you mention, using ROC curves results in a number of serious problems. Full post-test probability models are generally recommended, without dichotomization of post-test probabilities until the point at which individual-patient utilities can be estimated.
Frank Harrell
On Fri, 1 Feb 2002 14:27:07 -0600
"Cheng, Yiling" <ycheng at cooperinst.org> wrote:
> ROC (receiver operating characteristic) curve is used to determine of
> cutpoints for screening or diagnostic tests. It have been widely used in
> Epidemiology area. Usually we use logistic regression to get this kind
> curve. If anyone knows how to get this curve and do statistic test for two
> curve, do please let me know too. Thanks in advance.
> Yiling Cheng
>
> -----Original Message-----
> From: baron at cattell.psych.upenn.edu
> [mailto:baron at cattell.psych.upenn.edu]
> Sent: Friday, February 01, 2002 11:17 AM
> To: jg_liao at yahoo.com; r-help at stat.math.ethz.ch
> Subject: Re: [R] ROC curves using R
>
>
> What is it that you want to compute? I have a set of
> scripts - written by my colleaged David Brainard in
> Matlab and translated to R by me - which we used to teach
> signal-detection theory in a grad "methods" course.
> But they are very straightforward and not worth putting
> into a package. (I'm sure there is something much more
> sophisticated one can do, but you don't say what.)
>
> Some useful R functions (illustrated, and probably
> self-explanatory):
>
> zHit <- qnorm(Ph)
> zFa <- qnorm(Pfa)
> dPrime <- zHit-zFa
>
> zCr <- qnorm(1-Pfa)
> Beta <- dnorm(zCr,dPrime,1)/dnorm(zCr,0,1)
>
> And here is the optimal "beta" (criterion):
>
> logBetaOpt <- log((pn/ps)*(Vcr + Vfa)/(Vh + Vm))
>
> Jon Baron
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--
Frank E Harrell Jr Prof. of Biostatistics & Statistics
Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences
U. Virginia School of Medicine http://hesweb1.med.virginia.edu/biostat
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