perfDSC: Area Under the Precision-Recall Curve (AUPR), Belief...
In maPredictDSC: Phenotype prediction using microarray data: approach of the best overall team in the IMPROVER Diagnostic Signature Challenge

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/perfDSC.R

This function implements the three metrics used in the IMPROVER Diagnostic Signature Challenge.

1	perfDSC(pred,gs)

`pred`	A belief matrix, with rows coresponding to samples and columns to classes. The values are between 0 and 1 and sum on each row is 1. It needs to have row names. The belief values are the result of a prediction made by a model.
`gs`	A matrix, with rows coresponding to samples and columns to classes that give the true (gold standard) class membership of samples.

See cited documents for more details.

A named vector that includes the BCM, CCEM, AUPR_avg and Accuracy.

Adi Laurentiu Tarca <atarca@med.wayne.edu>

Adi L. Tarca, Mario Lauria, Michael Unger, Erhan Bilal, Stephanie Boue, Kushal Kumar Dey, Julia Hoeng, Heinz Koeppl, Florian Martin, Pablo Meyer, Preetam Nandy, Raquel Norel, Manuel Peitsch, Jeremy J Rice, Roberto Romero, Gustavo Stolovitzky, Marja Talikka, Yang Xiang, Christoph Zechner, and IMPROVER DSC Collaborators, Strengths and limitations of microarray-based phenotype prediction: Lessons learned from the IMPROVER Diagnostic Signature Challenge. Bioinformatics, submitted 2013.

predictDSC

#asume a 3 class classification problem; gs is the gold standard and pred are predictions
gs=cbind(A=c(1,1,1,1,0,0,0,0,0,0,0,0),B=c(0,0,0,0,1,1,1,1,0,0,0,0),C=c(0,0,0,0,0,0,0,0,1,1,1,1))
rownames(gs)<-paste("sample",1:12,sep="")
pred=cbind(A=c(0.6,0.9,1,0.3,0,0,0,0,0,0,0,0),B=c(0.4,0.1,0,0.7,1,1,0.7,1,0,0,0,0),C=c(0,0,0,0,0,0,0.3,0,1,1,1,1))
rownames(pred)<-paste("sample",1:12,sep="")
#male sure the sum per row is 1 is both gs and pred
apply(gs,1,sum)
apply(pred,1,sum)
#compute perfromance
perfDSC(pred,gs)