dI: Lower bound for the number of true discoveries

In angeella/ARIpermutation: Permutation-Based All-Resolutions Inference

Lower bound for the number of true discoveries

Description

Calculates 1-\alpha lower confidence bound for the set-wise of false null hypotheses.

Usage

dI(ix, cv, pvalues, iterative, approx, ncomb, ...)

Arguments

ix	Numeric vector: set-wise hypotheses considered.
cv	Numeric vector: critical vector computed by criticalVector.
pvalues	If iterative = TRUE you must put here the matrix of p-values with dimensions m \times B where m is the number of variables and B the number of permutations. Instead, if iterative = FALSE, you can put directly the vector of m observed p-values.
iterative	Boolean value. If iterative = TRUE, the iterative method is applied (computationally demanding). Default to FALSE. Please see the reference below.
approx	Boolean value. Default to TRUE. If you are analyzing high dimensional data, we suggest to put approx = TRUE to speed up the computation time. Please see the reference below.
ncomb	Numeric value. If approx = TRUE, you must decide how many random sub collections (level of approximation) considered. Default to 100.
...	Further arguments for the iterative approach, i.e., iterative = TRUE.

Value

Numeric value: the lower confidence bound for the number of true discoveries concerning the cluster ix specified.

Author(s)

Angela Andreella

References

Andreella, A., Hemerik, J., Finos, L., Weeda, W., & Goeman, J. (2023). Permutation-based true discovery proportions for functional magnetic resonance imaging cluster analysis. Statistics in Medicine, 42(14), 2311-2340.

Examples

db <- simulateData(pi0 = 0.7, m = 100, n = 20, rho = 0)
out <- signTest(X = db)
pv <- cbind(out$pv, out$pv_H0)
cv <- criticalVector(pvalues = pv, family = "simes", lambda = 0.1, alpha = 0.1)
dI(ix = c(1:100), cv = cv, pvalues = pv)

angeella/ARIpermutation documentation built on Sept. 12, 2024, 5:37 p.m.