Re: [R] ROCR for combination of markers

From: Frank Harrell <f.harrell_at_vanderbilt.edu>
Date: Thu, 28 Apr 2011 07:20:03 -0700 (PDT)

Be careful not to use cutpoints at any stage. improveProb does not require this. ROC analysis on the other hand is dangerous in the sense that it tempts one to find cutpoints, which are not replicable or consistent with decision theory.

Frank

Eik Vettorazzi wrote:
>
> Hi Rasanga,
> you may have a look at the 'improveProb' function from the Hmisc
> package. There you can compare the increase in prognostic power for
> several combinations of markers. You can create a ROC curve for a
> combination of markers by using the predicted risks eg. from a logistic
> regression model.
>
> To compare ROC curves of competing markers you can use 'roc.area.test'
> from the 'clinfun' package or 'hanley' from gcl.
>
> hth.
>
>
> Am 27.04.2011 23:17, schrieb Rasanga Ruwanthi:

>> Dear list
>>  
>> I have 5 markers that can be used to detect an infection in combination.
>> Could you please advise me how to use functions in ROCR/ other package to
>> produce the ROC curve for a combination of markers?
>>  
>> I have used the following to get ROC statistics for each marker.
>> pred <- prediction(y$marker1, y$infectn)
>> perf <-performance(pred,"tpr","fpr")
>> plot(perf,ave="threshold",spread.estimate="boxplot")
>> 
>> But I want know whether we could get this for more than one marker, so we
>> can look at how good the markers in combination to predict the infection.
>> I'm very grateful for any suggestion/help.
>>  
>> Thanks
>> Rasanga
>>  
>> 
>> 	[[alternative HTML version deleted]]
>> 
>> 
>> 
>> 
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>
> --
> Eik Vettorazzi
> Institut f├╝r Medizinische Biometrie und Epidemiologie
> Universit├Ątsklinikum Hamburg-Eppendorf
>
> Martinistr. 52
> 20246 Hamburg
>
> T ++49/40/7410-58243
> F ++49/40/7410-57790
>
> ______________________________________________
> R-help_at_r-project.org mailing list
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> PLEASE do read the posting guide
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>


Frank Harrell
Department of Biostatistics, Vanderbilt University
--
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