Description Usage Arguments Details Author(s) Examples

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.

1 2 |

`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 |

`name.file` |
a string giving the name of the png file to save the plot |

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

Yunting Sun yunting.sun@gmail.com, Nancy R.Zhang nzhang@stanford.edu, Art B.Owen owen@stanford.edu

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## 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)
``` |

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