true-discovery-guarantee: True discovery guarantee

In annavesely/sumSome: True Discovery Guarantee by Sum-Based Tests

True discovery guarantee

Description

These functions determine a lower confidence bound for the number of true discoveries, a lower confidence bound for the true discovery proportion (TDP), and an upper confidence bound for the false discovery proportion (FDP) within a set of interest. The bounds remain valid under post-hoc selection.

Usage

discoveries(object)

tdp(object)

fdp(object)

Arguments

object

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

Value

discoveries, tdp and fdp return a (1-alpha)-confidence bound for the corresponding quantity in the subset.

Author(s)

Anna Vesely.

See Also

Create a sumObj object: sumStats, sumPvals

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)

annavesely/sumSome documentation built on Jan. 28, 2025, 8:15 a.m.