pARI (version 1.1.3)

criticalVector: Critical vector

Description

Compute critical vector curve.

Usage

criticalVector(pvalues, family = "simes", alpha = 0.05, lambda, delta = 1, m = NULL)

Value

Numeric vector. Critical vector curve with length \(m\).

Arguments

pvalues

Matrix of pvalues with dimensions \(m \times B\) used instead of the data matrix X. Default to NULL.

family

String character. Name of the family confidence envelope to compute the critical vector from "simes", "aorc", "beta", "higher.criticism", and "power". Default to "simes".

alpha

Numeric value in `[0,1]`. \(\alpha\) level to control the family-wise error rate. Default to 0.05.

lambda

Numeric value. \(\lambda\) value computed by lambdaOpt. Default to 1.

delta

Numeric value. \(\delta\) value. Please see the reference below. Default to 1.

m

Numeric value. Number of hypothesis. Default to NULL.

Author

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.

See Also

lambdaOpt

Examples

db <- simulateData(pi0 = 0.8, m = 100, n = 20, rho = 0)
out <- pARI::signTest(X = db)
pv <- cbind(out$pv, out$pv_H0)
cv <- criticalVector(pvalues = pv, family = "simes", lambda = 1)
plot(sort(pv[,1]), type = "l")
lines(cv)