Simes on subsets, otherwise Bonferroni — simes.on.subsets.test • gMCPLite

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.

Value

adjusted p-value or decision of rejection

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))
#> [1] 0.2
simes.on.subsets.test(pvalues=c(0.1,0.2,0.05), weights=c(0.5,0.5,0), adjPValues=FALSE)
#> [1] 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-Result
#> 
#> Initial graph:
#> A graphMCP graph
#> H1 (weight=0.25)
#> H2 (weight=0.25)
#> H3 (weight=0.25)
#> H4 (weight=0.25)
#> Edges:
#> H1  -( 0.333333333333333 )->  H2 
#> H1  -( 0.333333333333333 )->  H3 
#> H1  -( 0.333333333333333 )->  H4 
#> H2  -( 0.333333333333333 )->  H1 
#> H2  -( 0.333333333333333 )->  H3 
#> H2  -( 0.333333333333333 )->  H4 
#> H3  -( 0.333333333333333 )->  H1 
#> H3  -( 0.333333333333333 )->  H2 
#> H3  -( 0.333333333333333 )->  H4 
#> H4  -( 0.333333333333333 )->  H1 
#> H4  -( 0.333333333333333 )->  H2 
#> H4  -( 0.333333333333333 )->  H3 
#> 
#> 
#> P-values:
#> [1] 0.01 0.05 0.03 0.02
#> 
#> Adjusted p-values:
#> [1] 0.04 0.06 0.06 0.06
#> 
#> Alpha: 0.05 
#> 
#> Hypothesis rejected:
#>    H1    H2    H3    H4 
#>  TRUE FALSE FALSE FALSE 
#> 
#> Final graph after1steps:
#> A graphMCP graph
#> H1 (rejected, weight=0)
#> H2 (weight=0.3333)
#> H3 (weight=0.3333)
#> H4 (weight=0.3333)
#> Edges:
#> H2  -( 0.5 )->  H3 
#> H2  -( 0.5 )->  H4 
#> H3  -( 0.5 )->  H2 
#> H3  -( 0.5 )->  H4 
#> H4  -( 0.5 )->  H2 
#> H4  -( 0.5 )->  H3 
#>