simes.on.subsets.test: Simes on subsets, otherwise Bonferroni
In gMCP: Graph Based Multiple Comparison Procedures

Description Usage Arguments Details Examples

Weighted Simes test introduced by Benjamini and Hochberg (1997)

1 2	simes.on.subsets.test(pvalues, weights, alpha = 0.05, adjPValues = TRUE, verbose = FALSE, subsets, subset, ...)

`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.

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

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))