Hi Pierre-Jean,
Sensitivity (Se) and specificity (Sp) are calculated for cutoffs stored in
the "performance" x.values of your prediction for Se and Sp:
For example, let's generate the performance for Se and Sp:
sens <- performance(pred,"sens")
spec <- performance(pred,"spec")
Now, you can have acces to:
[EMAIL PROTECTED] # (or [EMAIL PROTECTED]), which is the list of cutoffs
[EMAIL PROTECTED] # for the corresponding Se
[EMAIL PROTECTED] # for the corresponding Sp
You can for example sum up this information in a table:
(SeSp <- cbind ([EMAIL PROTECTED], [EMAIL PROTECTED],
[EMAIL PROTECTED]))
You can also write a function to give Se and Sp for a specific cutoff, but
you will have to define what to do for cutoffs not stored in the list. For
example, the following function keeps the closest stored cutoff to give
corresponding Se and Sp (but this is not always the best solution, you may
want to define your own way to interpolate):
se.sp <- function (cutoff, performance) {
sens <- performance(pred,"sens")
spec <- performance(pred,"spec")
num.cutoff <- which.min(abs([EMAIL PROTECTED] - cutoff))
return(list([EMAIL PROTECTED],
[EMAIL PROTECTED], [EMAIL PROTECTED]
[[1]][num.cutoff]))
}
se.sp(.5, pred)
Hope this helps,
Nael
On Thu, Nov 13, 2008 at 5:59 PM,
<[EMAIL PROTECTED]>wrote:
> Hi Frank,
>
> Thank you for your answer.
> In fact, I don't use this for clinical research practice.
> I am currently testing several scoring methods and I'd like
> to know which one is the most effective and which threshold
> value I should apply to discriminate positives and negatives.
> So, any idea for my problem ?
>
> Pierre-Jean
>
> -----Original Message-----
> From: Frank E Harrell Jr [mailto:[EMAIL PROTECTED]
> Sent: Thursday, November 13, 2008 5:00 PM
> To: Breton, Pierre-Jean-EXT R&D/FR
> Cc: r-help@r-project.org
> Subject: Re: [R] Calculate Specificity and Sensitivity for a given
> threshold value
>
> Kaliss wrote:
> > Hi list,
> >
> >
> > I'm new to R and I'm currently using ROCR package.
> > Data in input look like this:
> >
> > DIAGNOSIS SCORE
> > 1 0.387945
> > 1 0.50405
> > 1 0.435667
> > 1 0.358057
> > 1 0.583512
> > 1 0.387945
> > 1 0.531795
> > 1 0.527148
> > 0 0.526397
> > 0 0.372935
> > 1 0.861097
> >
> > And I run the following simple code:
> > d <- read.table("inputFile", header=TRUE); pred <- prediction(d$SCORE,
>
> > d$DIAGNOSIS); perf <- performance( pred, "tpr", "fpr");
> > plot(perf)
> >
> > So building the curve works easily.
> > My question is: can I have the specificity and the sensitivity for a
> > score threshold = 0.5 (for example)? How do I compute this ?
> >
> > Thank you in advance
>
> Beware of the utility/loss function you are implicitly assuming with
> this approach. It is quite oversimplified. In clinical practice the
> cost of a false positive or false negative (which comes from a cost
> function and the simple forward probability of a positive diagnosis,
> e.g., from a basic logistic regression model if you start with a cohort
> study) vary with the type of patient being diagnosed.
>
> Frank
>
> --
> Frank E Harrell Jr Professor and Chair School of Medicine
> Department of Biostatistics Vanderbilt
> University
>
> ______________________________________________
> R-help@r-project.org mailing list
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> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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