discrete.BH {DiscreteFDR} R Documentation

[HSU], [HSD], [AHSU] and [AHSD] procedures

Description

Apply the [HSU], [HSD], [AHSU] and [AHSD] procedures, with or without computing the critical constants, to a set of p-values and their discrete support.

Usage

discrete.BH(
raw.pvalues,
pCDFlist,
alpha = 0.05,
direction = "su",
ret.crit.consts = FALSE
)

DBH(
raw.pvalues,
pCDFlist,
alpha = 0.05,
direction = "su",
ret.crit.consts = FALSE
)

raw.pvalues,
pCDFlist,
alpha = 0.05,
direction = "su",
ret.crit.consts = FALSE
)


Arguments

 raw.pvalues vector of the raw observed p-values, as provided by the end user and before matching with their nearest neighbor in the CDFs supports. pCDFlist a list of the supports of the CDFs of the p-values. Each support is represented by a vector that must be in increasing order. alpha the target FDR level, a number strictly between 0 and 1. For *.fast kernels, it is only necessary, if stepUp = TRUE. direction a character string specifying whether to conduct a step-up (direction="su", by default) or step-down procedure (direction="sd"). adaptive a boolean specifying whether to conduct an adaptive procedure or not. ret.crit.consts a boolean. If TRUE, critical constants are computed and returned (this is computationally intensive).

Details

DBH and ADBH are wrapper functions for discrete.BH. DBH simply passes all its parameters to discrete.BH with adaptive = FALSE. ADBH does the same with adaptive = TRUE.

This version: 2019-06-18.

Value

A DiscreteFDR S3 class object whose elements are:

 Rejected Rejected raw p-values Indices Indices of rejected hypotheses Num.rejected Number of rejections Adjusted Adjusted p-values (only for step-down direction). Critical.constants Critical constants (if requested) Method Character string describing the used algorithm, e.g. 'Discrete Benjamini-Hochberg procedure (step-up)' Signif.level Significance level alpha Data$raw.pvalues The values of raw.pvalues Data$pCDFlist The values of pCDFlist Data$data.name The respective variable names of raw.pvalues and pCDFlist See Also kernel, DiscreteFDR, DBR Examples X1 <- c(4, 2, 2, 14, 6, 9, 4, 0, 1) X2 <- c(0, 0, 1, 3, 2, 1, 2, 2, 2) N1 <- rep(148, 9) N2 <- rep(132, 9) Y1 <- N1 - X1 Y2 <- N2 - X2 df <- data.frame(X1, Y1, X2, Y2) df #Construction of the p-values and their support df.formatted <- fisher.pvalues.support(counts = df, input = "noassoc") raw.pvalues <- df.formatted$raw
pCDFlist <- df.formatted$support DBH.su.fast <- DBH(raw.pvalues, pCDFlist) summary(DBH.su.fast) DBH.sd.fast <- DBH(raw.pvalues, pCDFlist, direction = "sd") DBH.sd.fast$Adjusted
summary(DBH.sd.fast)

DBH.su.crit <- DBH(raw.pvalues, pCDFlist, ret.crit.consts = TRUE)
summary(DBH.su.crit)
DBH.sd.crit <- DBH(raw.pvalues, pCDFlist, direction = "sd", ret.crit.consts = TRUE)
DBH.sd.crit$Adjusted summary(DBH.sd.crit) ADBH.su.fast <- ADBH(raw.pvalues, pCDFlist) summary(ADBH.su.fast) ADBH.sd.fast <- ADBH(raw.pvalues, pCDFlist, direction = "sd") ADBH.sd.fast$Adjusted