perfDSC {maPredictDSC} | R Documentation |
This function implements the three metrics used in the IMPROVER Diagnostic Signature Challenge.
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.
#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)