DiscreteFDR (version 1.3)

discrete.BH: [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",
  adaptive = FALSE, ret.crit.consts = FALSE)
DBH(raw.pvalues, pCDFlist, alpha = 0.05, direction = "su",
  ret.crit.consts = FALSE)
ADBH(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 neighbour 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).

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

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.

See Also

kernel, DiscreteFDR, DBR

Examples

# NOT RUN {
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.su.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
summary(ADBH.sd.fast)

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

# }