Weighted Test Functions for use with gMCP — weighted.test.functions • gMCPLite

The package gMCP provides the following weighted test functions:

bonferroni.test: Bonferroni test - see ?bonferroni.test for details.
parametric.test: Parametric test - see ?parametric.test for details.
simes.test: Simes test - see ?simes.test for details.
bonferroni.trimmed.simes.test: Trimmed Simes test for intersections of two hypotheses and otherwise Bonferroni - see ?bonferroni.trimmed.simes.test for details.
simes.on.subsets.test: Simes test for intersections of hypotheses from certain sets and otherwise Bonferroni - see ?simes.on.subsets.test for details.

Details

Depending on whether adjPValues==TRUE these test functions return different values:

If adjPValues==TRUE the minimal value for alpha is returned for which the null hypothesis can be rejected. If that's not possible (for example in case of the trimmed Simes test adjusted p-values can not be calculated), the test function may throw an error.
If adjPValues==FALSE a logical value is returned whether the null hypothesis can be rejected.

To provide your own test function write a function that takes at least the following 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 should not be used.
adjPValues: Logical scalar. If TRUE an adjusted p-value for the weighted test is returned (if possible - if not the function should call stop). Otherwise if adjPValues==FALSE a logical value is returned whether the null hypothesis can be rejected.
...: Further arguments possibly passed by gMCP which will be used by other test procedures but not this one.

Further the following parameters have a predefined meaning:

verbose: Logical scalar. If TRUE verbose output should be generated and printed to the standard output
subset
correlation

Author

Kornelius Rohmeyer rohmeyer@small-projects.de

Examples


# The test function 'bonferroni.test' is used in by gMCP in the following call:
graph <- BonferroniHolm(4)
pvalues <- c(0.01, 0.05, 0.03, 0.02)
alpha <- 0.05
r <- gMCP.extended(graph=graph, pvalues=pvalues, test=bonferroni.test, verbose=TRUE)
#> Info:
#> Subset {4}: rejected (adj-p: 0.02)
#> Subset {3}: rejected (adj-p: 0.03)
#> Subset {3,4}: rejected (adj-p: 0.04)
#> Subset {2}: rejected (adj-p: 0.05)
#> Subset {2,4}: rejected (adj-p: 0.04)
#> Subset {2,3}: not rejected (adj-p: 0.06)
#> Subset {2,3,4}: not rejected (adj-p: 0.06)
#> Subset {1}: rejected (adj-p: 0.01)
#> Subset {1,4}: rejected (adj-p: 0.02)
#> Subset {1,3}: rejected (adj-p: 0.02)
#> Subset {1,3,4}: rejected (adj-p: 0.03)
#> Subset {1,2}: rejected (adj-p: 0.02)
#> Subset {1,2,4}: rejected (adj-p: 0.03)
#> Subset {1,2,3}: rejected (adj-p: 0.03)
#> Subset {1,2,3,4}: rejected (adj-p: 0.04) 

# For the intersection of all four elementary hypotheses this results in a call
bonferroni.test(pvalues=pvalues, weights=getWeights(graph))
#> [1] 0.04
bonferroni.test(pvalues=pvalues, weights=getWeights(graph), adjPValues=FALSE)
#> [1] TRUE

# bonferroni.test function:
bonferroni.test <- function(pvalues, weights, alpha=0.05, adjPValues=TRUE, verbose=FALSE, ...) {
  if (adjPValues) {
    return(min(pvalues/weights))
  } else {
    return(any(pvalues<=alpha*weights))
  }
}