findClusters: Suprathreshold Clusters for Brain Imaging
In sumSome: Permutation True Discovery Guarantee by Sum-Based Tests

Description Usage Arguments Value Author(s) See Also Examples

This function determines spatially connected clusters, where t-scores are more extreme than a given threshold.

1	findClusters(copes, mask = NULL, thr = 3.2, alternative = "two.sided", silent = FALSE)

`copes`	list of 3D numeric arrays (contrasts maps for each subject).
`mask`	3D logical array, where `TRUE` values correspond to voxels inside the brain, or character for a Nifti file name.
`thr`	threshold.
`alternative`	direction of the alternative hypothesis (`greater`, `lower`, `two.sided`).
`silent`	logical, `FALSE` to print the number of clusters.

findClusters returns a 3D numeric array, with integer values corresponding to clusters, and 0 to other voxels.

Anna Vesely.

Permutation statistics for brain imaging: brainScores, brainPvals

True discovery guarantee for cluster analysis: clusterAnalysis

# simulate 20 copes with dimensions 10x10x10
set.seed(42)
copes <- list()
for(i in seq(20)){copes[[i]] <- array(rnorm(10^3, mean = -10, sd = 30), dim=c(10,10,10))}

# cluster map where t scores are grater than 2.8, in absolute value
thr <- 2.8
cl <- findClusters(copes = copes, thr = thr)

# create object of class sumBrain
res <- brainScores(copes = copes, alpha = 0.2, seed = 42, truncFrom = thr)
res
summary(res)

# confidence bound for the number of true discoveries and the TDP within clusters
out <- clusterAnalysis(res, clusters = cl$clusters)