pARIbrain {pARI} | R Documentation |
Permutation-based All-Resolutions Inference for brain imaging.
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
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., contrasts maps, one for each subject used to compute the statistical tests. |
thr |
Numeric value. Threshold used to construct the cluster map. Default to |
mask |
NIfTI file or character string. 3D array of logical values (i.e. |
alpha |
Numeric value in '[0,1]'. |
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 |
alternative |
Character string. It refers to the alternative hypothesis, must be one of |
summary_stat |
Character string. Choose among |
silent |
Boolean value. Default to |
family |
String character. Name of the family confidence envelope to compute the critical vector
from |
delta |
Numeric value. |
B |
Numeric value. Number of permutations, default to 1000. |
rand |
Boolean value. Default to |
iterative |
Boolean value. If |
approx |
Boolean value. Default to |
ncomb |
Numeric value. If |
step.down |
Boolean value. Default to |
max.step |
Numeric value. Default to 10. Maximum number of steps for the step down approach, so useful when |
... |
further arguments. See |
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, 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
signTest
, lambdaOpt
, criticalVector
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)