leapp (version 1.3)

ROCplot: plot ROC curve

Description

Input an p by d matrix, each column of which contains false positive rates(FPR) computed from each of the d methods and p significance levels and a matrix of corresponding true positive rates(TPR) at the same significance levels. Plot ROC curve for each of the d methods.

Usage

ROCplot(fpr,tpr,main, name.method, 
         xlim = c(0,0.2),ylim = c(0.4,1), save = TRUE, name.file = NULL)

Arguments

fpr

A matrix of false positive rates for increasing sizes of retrieved significant genes

tpr

A vector of corresponding true positive rates at the same significance levels

main

a string, title of the figure

name.method

a string vector of length d containing names of the d methods

xlim

the range of the x axis(FPR), default to c(0,0.2)

ylim

the range of the y axis (TPR), default to c(0.4,1)

save

a logical value, if TRUE, will save the plot to an png file, default to TRUE

name.file

a string giving the name of the png file to save the plot

Details

The order of the name.method should be the same as that in the fpr and tpr.

Examples

# NOT RUN {
  
# }
# NOT RUN {
   library(sva)
   library(MASS)
   library(leapp)
   data(simdat)
   model <- cbind(rep(1,60),simdat$g)
   model0 <- cbind(rep(1,60))
   p.raw <- f.pvalue(simdat$data,model,model0)
   p.oracle <-f.pvalue(simdat$data - simdat$u<!-- %*%t(simdat$v), model, model0) -->
    
   p.leapp <- leapp(simdat$data,pred.prim = simdat$g, method = "hard")$p
   p = cbind(p.raw,p.oracle, p.leapp)
   topk = seq(0,0.5,length.out = 50)*1000
   null.set = which(simdat$gamma !=0)
   fpr= apply(p,2,FindFpr,null.set,topk)
   tpr= apply(p,2,FindTpr,null.set,topk)
   ROCplot(fpr,tpr, main = "ROC Comparison",
           name.method = c("raw","oracle","leapp"), save = FALSE )
 
# }