Description Usage Arguments Value Author(s) Examples
Main function to perform Clusterwise Independent Component Analysis
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DataList |
a list of matrices |
nStarts |
number of multiple starts |
nComp |
number of ICA components per cluster |
nClus |
number of clusters |
scale |
scale each matrix to have an equal sum of squares |
center |
mean center matrices |
rational |
a rational starting seed, if NULL no rational starting seed is used |
maxiter |
maximum number of iterations for each start |
verbose |
print loss information to console |
CICA
returns an object of class
"CICA". It contains the estimated clustering, cluster specific component matrices and subject specific time course matrices
P |
partitioning vector of size |
Sr |
list of size |
Ais |
list of size |
Loss |
loss function value of the best start |
LossStarts |
loss function values of all starts |
Jeffrey Durieux
1 2 3 | data('ExampleData', package = 'CICA')
output <- CICA(DataList = ExampleData, nStarts = 3, nComp = 5, nClus = 3, verbose = FALSE)
summary(output)
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