clusterExt | R Documentation |
Given a NxV imaging matrix Y (N = number of subjects, V = number of vertices in the ventricular mesh), a NxC model matrix X (N = number of subjects, C = number of variables + intercept term) and the number of the column variables to extract, this function computes whether a vertex belongs to a significant cluster or not using a cluster-extend based thresholding method. The output is an array which stores as 1 the vertices that reached significance, 0 otherwise.
clusterExt(X, Y, extract, A, NNmatrix, nPermutations = 1000, HC4m = FALSE,
parallel = FALSE, nCores = 1, thrFirst = 1)
X |
is the design matrix. Number of rows = number of subjects in the study, number of columns = number of vertices in the atlas. Numerical varable must be normalized to 0-mean and unit-standard deviation. Categorical variables must be coded using dummy coding. The first column should contain the intercept (all 1s). |
Y |
is the imaging matrix. Number of rows = N. Number of columns = V. |
extract |
is an array expressing which covariates in X you want to extract. |
A |
A V-dimensional vector containing the area associated with a vertex, usually its Voronoi area. |
NNmatrix |
Nx2 matrix containing the mesh edges. Important: to speed up the execution please avoid repetitions like (A,B) and (B,A). |
nPermutations |
number of permutations in the permutation test, default is 1000. |
HC4m |
flag for triggering HC4m correction, default is FALSE. |
parallel |
flag for triggering parallel computing, default is FALSE. |
nCores |
flag for defining the number of cores to use, default is 1. |
firsThr |
the cluster-forming threshold. |
The output of this function contains a list of the vertices that reached significace, 0 otherwise.
res = clusterExt(X, Y, extract, A, NNmatrix, nPermutations = 1000, HC4m = TRUE, nCores=1, thrFirst = 1)
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