continuous.GR: Continuous Guo-Romano procedure
In FDX: False Discovery Exceedance Controlling Multiple Testing Procedures

continuous.GR

R Documentation

Continuous Guo-Romano procedure

Description

Apply the usual continuous [GR] procedure, with or without computing the critical values, to a set of p-values. A non-adaptive version is available as well.

Usage

continuous.GR(
  raw.pvalues,
  alpha = 0.05,
  zeta = 0.5,
  adaptive = TRUE,
  critical.values = FALSE
)

GR(raw.pvalues, alpha = 0.05, zeta = 0.5, critical.values = FALSE)

NGR(raw.pvalues, alpha = 0.05, zeta = 0.5, critical.values = 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.
`alpha`	the target FDP, a number strictly between 0 and 1. For `*.fast` kernels, it is only necessary, if `stepUp = TRUE`.
`zeta`	the target probability of not exceeding the desired FDP, a number strictly between 0 and 1. If `zeta=NULL` (the default), then `zeta` is chosen equal to `alpha`.
`adaptive`	a boolean specifying whether to conduct an adaptive procedure or not.
`critical.values`	a boolean. If `TRUE`, critical constants are computed and returned (this is computationally intensive).

Details

GR and NGR are wrapper functions for continuous.GR. The first one simply passes all its parameters to continuous.GR with adaptive = TRUE and NGR does the same with adaptive = FALSE.

Value

A FDX 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.values`	Critical values (if requested).
`Method`	A character string describing the used algorithm, e.g. 'Discrete Lehmann-Romano procedure (step-up)'.
`FDP.threshold`	FDP threshold `alpha`.
`Exceedance.probability`	Probability `zeta` of FDP exceeding `alpha`; thus, FDP is being controlled at level `alpha` with confidence `1 - zeta`.
`Adaptive`	A boolean specifying whether an adaptive procedure was conducted or not.
`Data$raw.pvalues`	The values of `raw.pvalues`.
`Data$data.name`	The respective variable names of `raw.pvalues` and `pCDFlist`.

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 supports (fisher.pvalues.support
# is from 'DiscreteFDR' package!)
df.formatted <- fisher.pvalues.support(counts = df, input = "noassoc")
raw.pvalues <- df.formatted$raw
pCDFlist <- df.formatted$support

GR.fast <- GR(raw.pvalues)
summary(GR.fast)

GR.crit <- GR(raw.pvalues, critical.values = TRUE)
summary(GR.crit)

NGR.fast <- NGR(raw.pvalues)
summary(NGR.fast)

NGR.crit <- NGR(raw.pvalues, critical.values = TRUE)
summary(NGR.crit)

FDX documentation built on Oct. 18, 2022, 9:09 a.m.