simes.on.subsets.test: Simes on subsets, otherwise Bonferroni

In gMCP: Graph Based Multiple Comparison Procedures

Simes on subsets, otherwise Bonferroni

Description

Weighted Simes test introduced by Benjamini and Hochberg (1997)

Usage

simes.on.subsets.test(
  pvalues,
  weights,
  alpha = 0.05,
  adjPValues = TRUE,
  verbose = FALSE,
  subsets,
  subset,
  ...
)

Arguments

pvalues	A numeric vector specifying the p-values.
weights	A numeric vector of weights.
alpha	A numeric specifying the maximal allowed type one error rate. If adjPValues==TRUE (default) the parameter alpha is not used.
adjPValues	Logical scalar. If TRUE (the default) an adjusted p-value for the weighted test is returned. Otherwise if adjPValues==FALSE a logical value is returned whether the null hypothesis can be rejected.
verbose	Logical scalar. If TRUE verbose output is generated.
subsets	A list of subsets given by numeric vectors containing the indices of the elementary hypotheses for which the weighted Simes test is applicable.
subset	A numeric vector containing the numbers of the indices of the currently tested elementary hypotheses.
...	Further arguments possibly passed by gMCP which will be used by other test procedures but not this one.

Details

As an additional argument a list of subsets must be provided, that states in which cases a Simes test is applicable (i.e. if all hypotheses to test belong to one of these subsets), e.g. subsets <- list(c("H1", "H2", "H3"), c("H4", "H5", "H6")) Trimmed Simes test for intersections of two hypotheses and otherwise weighted Bonferroni-test

Examples

simes.on.subsets.test(pvalues=c(0.1,0.2,0.05), weights=c(0.5,0.5,0))
simes.on.subsets.test(pvalues=c(0.1,0.2,0.05), weights=c(0.5,0.5,0), adjPValues=FALSE)

graph <- BonferroniHolm(4)
pvalues <- c(0.01, 0.05, 0.03, 0.02)

gMCP.extended(graph=graph, pvalues=pvalues, test=simes.on.subsets.test, subsets=list(1:2, 3:4))

gMCP documentation built on May 29, 2024, 9:37 a.m.