mirandom: Convert target/decoy scores into winning scores and labels

In uni-Arya/stepdownfdp: A Step-Down Procedure to Control the False Discovery Proportion

Convert target/decoy scores into winning scores and labels

Description

mirandom takes a collection of target and decoy scores for m hypotheses and returns a winning score and label attached to each. The argument scores must be organised in an m x (d + 1) matrix, where d is the number of decoy scores. The first column of scores must contain the target scores.

Usage

mirandom(scores, c = 0.5, lambda = 0.5)

Arguments

scores	An m x (d + 1) matrix where m is the number of hypothesis and d is the number of decoy scores for each hypothesis. The first column of scores are target scores and subsequent columns are decoy scores.
c	Determines the ranks of the target score that are considered winning. Defaults to c = 0.5 for single-decoy FDP-SD.
lambda	Determines the ranks of the target score that are considered losing. Defaults to lambda = 0.5 for single-decoy FDP-SD.

Value

A m x 2 matrix where m is the number of hypotheses. The first column contains the winning scores and the second column contains the corresponding labels.

Examples

target_scores <- rnorm(200, mean = 1.5)
decoy_scores <- matrix(rnorm(600, mean = 0), ncol = 3)
scores <- cbind(target_scores, decoy_scores)
mirandom(scores)

uni-Arya/stepdownfdp documentation built on March 19, 2022, 8:19 a.m.