tdp {sumSome}

Confidence Bound for the TDP

Description

This function determines a lower confidence bound for the true discovery proportion within a set of interest. The bound remains valid under post-hoc selection.

Usage

tdp(object)

## S3 method for class 'sumObj'
tdp(object)

Arguments

object

an object of class sumObj, as returned by the functions sumStats and sumPvals.

Value

tdp returns a lower (1-alpha)-confidence bound for the true discovery proportion in the set.

Author(s)

Anna Vesely.

References

Goeman, J. J. and Solari, A. (2011). Multiple testing for exploratory research. Statistical Science, 26(4):584-597.

Hemerik, J. and Goeman, J. J. (2018). False discovery proportion estimation by permutations: confidence for significance analysis of microarrays. JRSS B, 80(1):137-155.

Vesely, A., Finos, L., and Goeman, J. J. (2020). Permutation-based true discovery guarantee by sum tests. Pre-print arXiv:2102.11759.

See Also

Create a sumObj object: sumStats, sumPvals

Lower confidence bound for the number of true discoveries: discoveries

Upper confidence bound for the FDP: fdp

Examples

# generate matrix of p-values for 5 variables and 10 permutations
G <- simData(prop = 0.6, m = 5, B = 10, alpha = 0.4, seed = 42)

# subset of interest (variables 1 and 2)
S <- c(1,2)
 
# create object of class sumObj
# combination: harmonic mean (Vovk and Wang with r = -1)
res <- sumPvals(G, S, alpha = 0.4, r = -1)
res
summary(res)

# lower confidence bound for the number of true discoveries in S
discoveries(res)

# lower confidence bound for the true discovery proportion in S
tdp(res)

# upper confidence bound for the false discovery proportion in S
fdp(res)