getRoc: Operating characteristics of differential expression analysis
In casper: Characterization of Alternative Splicing based on Paired-End Reads

Description Usage Arguments Value Methods Author(s) Examples

getRoc compares simulation truth and data analysis results to determine False Positives (FP), False Negatives (FP), True Positives (TP), True Negatives (TN), Positives (FP+TP), False Discovery Proportion (FP/P) and Power (TP/(TP+FN)).

1	getRoc(simTruth, decision)

`simTruth`	Binary vector or matrix indicating simulation truth (`FALSE` or 0 for non differential expression, `TRUE` or 1 for differential expression)
`decision`	Binary vector or matrix with differential expression calls based on some data analysis.

data.frame with TP, FP, TN, FN, P, FDR and Power.

signature(simTruth='logical',decision='logical'): Operating characteristics are computed for a single simulation
signature(simTruth='numeric',decision='numeric'): Operating characteristics are computed for a single simulation
signature(simTruth='matrix',decision='matrix'): simTruth and decision contain truth and calls for several simulations (in columns). getRoc returns a data.frame with operating characteristics in each simulation.