SparseNeuroVec-class | R Documentation |
A class representing a sparse four-dimensional brain image, backed by a matrix
, where each column represents a non-zero vector spanning the fourth dimension (e.g., time) and defined by a volumetric mask. SparseNeuroVec objects store their data in a compressed format, providing efficient storage and access to sparse brain images. This class inherits from the NeuroVec
, AbstractSparseNeuroVec
, and implements the ArrayLike4D
interface.
Constructs a SparseNeuroVec object for efficient representation and manipulation of sparse neuroimaging data with many zero or missing values.
SparseNeuroVec(data, space, mask)
data |
A matrix or a 4-D array containing the neuroimaging data. The dimensions of the data should be consistent with the dimensions of the provided NeuroSpace object and mask. |
space |
A NeuroSpace object representing the dimensions and voxel spacing of the neuroimaging data. |
mask |
A 3D array, 1D vector of type logical, or an instance of type LogicalNeuroVol, which specifies the locations of the non-zero values in the data. |
A SparseNeuroVec object, containing the sparse neuroimaging data, mask, and associated NeuroSpace information.
data
A matrix
of series, where rows span across voxel space and columns span the fourth dimension. Each column represents a non-zero vector in the 4D space, and the matrix stores only non-zero values to save memory.
bspace <- NeuroSpace(c(10,10,10,100), c(1,1,1))
mask <- array(rnorm(10*10*10) > .5, c(10,10,10))
mat <- matrix(rnorm(sum(mask)), 100, sum(mask))
svec <- SparseNeuroVec(mat, bspace, mask)
length(indices(svec)) == sum(mask)
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