ROCplot: plot ROC curve

In leapp: Latent Effect Adjustment After Primary Projection

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.

Author(s)

Yunting Sun [e-mail], Nancy R.Zhang [e-mail], Art B.Owen [e-mail]

Examples

  ## 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
    
   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 )
 
## End(Not run)

leapp documentation built on June 20, 2022, 1:05 a.m.