pARI: Permutation-based All-Resolutions Inference

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

Permutation-based All-Resolutions Inference

Description

The main function for All-Resolutions Inference (ARI) method based on the critical vector constructed using the p-values permutation distribution. The function computes simultaneous lower bounds for the number of true discoveries for each set of hypotheses specified in ix controlling family-wise error rate at level alpha.

Usage

pARI(X= NULL, ix, alpha = 0.05, family = "simes", delta = 0, B = 1000, pvalues = NULL, 
test.type = "one_sample", complete = FALSE, clusters = FALSE, iterative = FALSE, 
approx = TRUE, ncomb = 100, step.down = FALSE, max.step = 10, ...)

Arguments

X	Data matrix where rows represent the m variables and columns the n observations.
ix	Numeric vector which expresses the set of hypotheses of interest. It can be a vector with length equals m indicating the corresponding cluster for each variable, (in this case, you must put clusters = TRUE), or a vector containing the position indices of the variables of interest if only one set/cluster of hypotheses is considered.
alpha	Numeric value in '[0,1]'. \alpha level to control the family-wise error rate. Default to 0.05.
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".
delta	Numeric value. \delta value. Please see the reference below. Default to 0.
B	Numeric value. Number of permutations, default to 1000.
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.
test.type	Character string. Choose a type of tests among "one_sample", i.e., one-sample t-tests, or "two_samples", i.e., two-samples t-tests. Default "one_sample".
complete	Boolean value. If TRUE the sets of critical vectors and the raw p-values are returned. Default to FALSE.
clusters	Boolean value. If ix indicates many clusters/sets must be TRUE. Default @FALSE.
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.
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.
...	Further arguments

Value

by default returns a list with the following objects:

discoveries

lower bound for the number of true discoveries in the set selected

ix

selected variables

If complete = TRUE the raw pvalues and cv critical vector are also returned.

Author(s)

Angela Andreella

References

For the general framework of All-Resolutions Inference see:

Goeman, Jelle J., and Aldo Solari. "Multiple testing for exploratory research." Statistical Science 26.4 (2011): 584-597.

For permutation-based All-Resolutions Inference see:

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

The type of tests implemented: signTest permTest.

Examples

datas <- simulateData(pi0 = 0.8, m = 1000, n = 30, power = 0.9, rho = 0,seed = 123)
out <- pARI(X = datas, ix = c(1:200),test.type = "one_sample")
out

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