pARIbrain: Permutation-based All-Resolutions Inference for brain...

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

Permutation-based All-Resolutions Inference for brain imaging.

Description

The main function for brain imaging All-Resolutions Inference (ARI) method based on critical vectors 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.

Usage

pARIbrain(copes, thr=NULL, mask=NULL, alpha=.05, clusters = NULL, 
alternative = "two.sided", summary_stat=c("max", "center-of-mass"),
silent=FALSE, family = "simes", delta = 0, B = 1000, rand = FALSE, 
iterative = FALSE, approx = TRUE, ncomb = 100, step.down = FALSE, max.step = 10, ...)

Arguments

copes	list of NIfTI file. The list of copes, i.e., constrasts maps, one for each subject used to compute the statistical tests.
thr	numeric value. Threshold used to construct the cluster map. Default @NULL.
mask	NIfTI file or character string. 3D array of logical values (i.e. TRUE/FALSE in/out of the brain). Alternatively it may be a (character) NIfTI file name. If mask=NULL, it is assumed that non of the voxels have to be excluded.
alpha	numeric value in '[0,1]'. It expresses the alpha level to control the family-wise error rate. Default 0.05.
clusters	NIfTI file or character string. 3D array of cluster ids (0 when voxel does not belong to any cluster) or a (character) NIfTI file name. If cluster=NULL the cluster map is computed by the cluster_threshold function with threshold equals thr.
alternative	character string. It refers to the alternative hypothesis, must be one of "two.sided" (default), "greater" or "lower".
summary_stat	character string. Choose among =c("max", "center-of-mass").
silent	Boolean value. Default @FALSE. If @TRUE the function prints the results.
family	string character. Choose a family of confidence envelopes to compute the critical vector from "simes", "aorc", "beta" and "higher.criticism".#' @param alpha alpha level.
delta	numeric value. It expresses the delta value, please see the references. Default to 0.
B	numeric value. Number of permutations, default to 1000.
rand	Boolean value. Default @FALSE. If rand = TRUE, the p-values are computed by rowRanks.
iterative	Boolean value. If iterative = TRUE, the iterative method for improvement of confidence envelopes is applied. Default @FALSE.
approx	Boolean value. Default @TRUE. If you are treating high dimensional data, we suggest to put approx = TRUE to speed up the computation time.
ncomb	Numeric value. If approx = TRUE, you must decide how many random subcollections (level of approximation) considered.
step.down	Boolean value. Default @FALSE If you want to compute the lambda calibration parameter using the step-down approach put @TRUE.
max.step	Numeric value. Default to 10. Maximum number of steps for the step down approach, so useful when step.down = TRUE.
...	further arguments. See signTest.

Value

A list with elements - out: data.frame containing the size, the number of false null hypotheses, the number of true null hypotheses, the lower bound for the true discovery proportion, and other statistics for each cluster. - clusters: matrix describing the clusters analyzed.

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 All-Resolutions Inference for functional Magnetic Resonance Imaging data see:

Rosenblatt, Jonathan D., et al. "All-resolutions inference for brain imaging." Neuroimage 181 (2018): 786-796.

For permutation-based All-Resolutions Inference see:

Andreella, Angela, et al. "Permutation-based true discovery proportions for fMRI cluster analysis." arXiv preprint arXiv:2012.00368 (2020).

Examples

## Not run: 
library(remotes)
install_github("angeella/fMRIdata")
library(fMRIdata)
data(Auditory_clusterTH3_2)
data(Auditory_copes)
data(Auditory_mask)
auditory_out <- pARIbrain(copes = Auditory_copes, 
clusters = Auditory_clusterTH3_2, mask = Auditory_mask, 
alpha = 0.05, silent = TRUE)
auditory_out$out

## End(Not run)

angeella/ARIpermutation documentation built on Aug. 24, 2023, 3:36 p.m.