pARI (version 1.1.3)

lambdaOpt: Lambda calibration

Description

Computes the optimal lambda calibration parameter used in the critical vector criticalVector.

Usage

lambdaOpt(pvalues, family, alpha = 0.05, delta = 0, step.down = FALSE,
 max.step = 10, m = NULL)

Value

Numeric value. \(\lambda\) parameter estimate.

Arguments

pvalues

Matrix of \(p\)-values with dimensions \(m \times B\) where \(m\) is the number of variables and \(B\) the number of permutations 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.

delta

Numeric value. \(\delta\) value. Please see the reference below. Default to 0.

step.down

Boolean value. Default to FALSE If you want to compute the lambda calibration parameter using the step-down approach put TRUE. Please see the reference below.

max.step

Numeric value. Default to 10. Maximum number of steps for the step down approach, so useful when step.down = TRUE.

m

Numeric value. Number of hypotheses. 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

criticalVector

Examples

db <- simulateData(pi0 = 0.8, m = 100, n = 20, rho = 0)
out <- signTest(X = db)
pv <- cbind(out$pv, out$pv_H0)
cv <- lambdaOpt(pvalues = pv, family = "simes", alpha = 0.05)