sumStatsPar: True Discovery Guarantee for Generic Statistics - Parametric

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

True Discovery Guarantee for Generic Statistics - Parametric

Description

This function uses generic statistics and a suitable vector of critical values to determine confidence bounds for the number of true discoveries, the true discovery proportion and the false discovery proportion within a set of interest. The bounds are simultaneous over all sets, and remain valid under post-hoc selection.

Usage

sumStatsPar(g, S = NULL, alpha = 0.05, cvs)

Arguments

g	numeric vector of statistics.
S	vector of indices for the variables of interest (if not specified, all variables).
alpha	significance level.
cvs	numeric vector of critical values for summed statistics considering 1:m hypotheses.

Value

sumStatsPar returns an object of class sumObj, containing

• total: total number of variables (length of g)

• size: size of S

• alpha: significance level

• TD: lower (1-alpha)-confidence bound for the number of true discoveries in S

• maxTD: maximum value of TD that could be found under convergence of the algorithm

• iterations: number of iterations of the algorithm (NULL)

Author(s)

Xu Chen.

References

Goeman J. J. and Solari A. (2011). Multiple testing for exploratory research. Statistical Science, doi: 10.1214/11-STS356.

Tian J., Chen X., Katsevich E., Goeman J. J. and Ramdas A. (2022). Large-scale simultaneous inference under dependence. Scandinavian Journal of Statistics, doi: 10.1111/sjos.12614.

See Also

True discovery guarantee using p-values (parametric): sumPvalsPar

Access a sumObj object: discoveries, tdp, fdp

Examples

# generate vector of statistics for 5 variables (Fisher transformation of p-values)
g <- as.vector(simData(prop = 0.6, m = 5, B = 1, alpha = 0.4, seed = 42))
g <- -2 * log(g)

# subset of interest (variables 1 and 2)
S <- c(1,2)

# vector of critical values
cvs <- qchisq(p = 0.4, df = 2 * seq(5), lower.tail=FALSE)
 
# create object of class sumObj
res <- sumStatsPar(g, S, alpha = 0.4, cvs = cvs)
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.