R help - Nov 2008 - Calculate Specificity and Sensitivity for a given threshold value

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
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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