performance.eval: This function estimates the different performance indices...

In rMisbeta: A Robust Missing Imputation Method for Gene Expression Data

Description

This function estimates the different performance indeces,like TPR,TNR,FPR,FNR,AUC etc. to asses the performance of the method

Usage

performance.eval(PostP, de.true, TopG, decreasing = TRUE)

Arguments

PostP	p-values should be given to identify the different performance index.
de.true	The true DE information should be given to calculates the performance index.
TopG	How many Top DE genes will be used to calculate the performance indices.
decreasing	Is the p-values decreasing or increasing order.

Value

The following performance indices are produced by performance.eval():

TP	Number of True positive.
TN	Number of True negative.
FP	Number of False positive.
FN	Number of False negative.
R1	Specificity.
TPR	True positive rate.
TNR	True negative rate.
FPR	False positive rate.
FNR	False negative rate.
FDR	False discovery rate.
ER	Error rate.
AUC2	Area under the curve of ROC.
pAUC2	Partial Area under the curve of ROC with FDR controlled at 0.2.

Author(s)

Md.Shahjaman and Md. Nurul Haque Mollah [e-mail]

Examples

# Performance evaluation in presence of outliers and missing values
nG=1000
n1=n2=5
pde=0.1
Simdat=Sim2Group(nG,n1,n2,var0=0.1,pde=0.1) 
xx=Simdat$outmat
TrueDE=Simdat$DEtrue
MisOutdat<-OutMisDat(xx,pctOut=0.1,pctMis=0.1)
misdat_zero<-MisOutdat
misdat_zero[is.na(misdat_zero)]<-0
cl=rep(c(1,2),each=n1)

res=remat(MisOutdat,cl)
up_mat<-res$remat


pTtest_zero<-pTtest_beta<-NULL
for (j1 in 1:dim(xx)[1])
{
DataYY <- data.frame(YY =misdat_zero[j1,], FactorLevels = factor(cl))
DataYY2 <- data.frame(YY2=up_mat[j1,], FactorLevels2 = factor(cl))
pTtest_zero[j1] <-  t.test(YY~FactorLevels,data=DataYY, paired=FALSE)[[3]]
pTtest_beta[j1] <-  t.test(YY2~FactorLevels2,data=DataYY2, paired=FALSE)[[3]]
} 

TopDEn<-seq(nG*pde/10, pde*nG, length=10)

performance_zero<-performance.eval(pTtest_zero,TrueDE,TopDEn,decreasing=FALSE);
performance_beta<-performance.eval(pTtest_beta,TrueDE,TopDEn,decreasing=FALSE);
plot(performance_zero$FPR,performance_zero$TPR,type="o",  
xlab="False Positive Rate",ylab="True Positive Rate",ylim=c(0,1))
points(performance_beta$FPR,performance_beta$TPR,type="o",col=2)
legend("bottomright", c('t_test_zero','t_test_rMisbeta'),lwd=1,cex=0.8,col=c(1,2))

rMisbeta documentation built on Jan. 13, 2021, 6:28 a.m.