FDX (version 1.0.2)

continuous.GR: 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).

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.

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.

See Also

kernel, FDX-package, continuous.LR, discrete.LR, discrete.GR, discrete.PB, weighted.LR, weighted.GR, weighted.PB

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 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)

# }