CODEWITHSUNDEEP
proc-r-package
sam
sam

Reputation: 21

What are the algorithms used in the pROC package for ROC analysis?

I am trying to figure out what algorithms are used within the pROC package to conduct ROC analysis. For instance what algorithm corresponds to the condition 'algorithm==2'? I only recently started using R in conjunction with Python because of the ease of finding CI estimates, significance test results etc. My Python code uses Linear Discriminant Analysis to get results on a binary classification problem. When using the pROC package to compute confidence interval estimates for AUC, sensitivity, specificity, etc., all I have to do is load my data and run the package. The AUC I get when using pROC is the same as the AUC that is returned by my Python code that uses Linear Discriminant Analysis (LDA). In order to be able to report consistent results I am trying to find out if LDA is one of the algorithm choices within pROC? Any ideas on this or how to go about figuring this out would be very helpful. Where can I access the source code for pROC?

Upvotes: 2

Views: 993

Answers (1)

Calimo
Calimo

Reputation: 7969

The core algorithms of pROC are described in a 2011 BMC Bioinformatics paper. Some algorithms added later are described in the PDF manual. As every CRAN package, the source code is available from the CRAN package page. As many R packages these days it is also on GitHub.

To answer your question specifically, unfortunately I don't have a good reference for the algorithm to calculate the points of the ROC curve with algorithm 2. By looking at it you will realize it is ultimately equivalent to the standard ROC curve algorithm, albeit more efficient when the number of thresholds increases, as I tried to explain in this answer to a question on Cross Validated. But you have to trust me (and most packages calculating ROC curves) on it.

Which binary classifier you use, whether LDA or other, is irrelevant to ROC analysis, and outside the scope of pROC. ROC analysis is a generic way to assesses predictions, scores, or more generally signal coming out of a binary classifier. It doesn't assess the binary classifier itself, or the signal detector, only the signal itself. This makes it very easy to compare different classification methods, and is instrumental to the success of ROC analysis in general.

Upvotes: 1

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