CICA: CICA: Clusterwise Independent Component Analysis

In CICA: Clusterwise Independent Component Analysis

CICA: Clusterwise Independent Component Analysis

Description

Main function to perform Clusterwise Independent Component Analysis

Usage

CICA(
  DataList,
  nComp,
  nClus,
  method = "fastICA",
  RanStarts,
  RatStarts = NULL,
  pseudo = NULL,
  pseudoFac,
  userDef = NULL,
  userGrid = NULL,
  scalevalue = 1000,
  center = TRUE,
  maxiter = 100,
  verbose = TRUE,
  ctol = 1e-06,
  checks = TRUE
)

Arguments

DataList	a list of matrices
nComp	number or vector of ICA components per cluster
nClus	number or vector of clusters
method	Component method, default is fastICA. EVD for a fast eigen value based estimation
RanStarts	number of random starts
RatStarts	Generate rational starts. Either 'all' or a specific linkage method name (e.g., 'complete'). Use NULL to indicate that Rational starts should not be used.
pseudo	percentage value for perturbating rational starts to obtain pseudo rational starts
pseudoFac	factor to multiply the number of rational starts (7 in total) to obtain pseudorational starts
userDef	a user-defined starting seed stored in a data.frame, if NULL no userDef starting partition is used
userGrid	user supplied data.frame for multiple model CICA. First column are the requested components. Second column are the requested clusters
scalevalue	desired sum of squares of the block scaling procedure
center	mean center matrices
maxiter	maximum number of iterations for each start
verbose	print loss information to console
ctol	tolerance value for convergence criterion
checks	boolean parameter that indicates whether the input checks should be run (TRUE) or not (FALSE).

Value

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 length(DataList)
Sr	list of size nClus, containing cluster specific independent components
Ais	list of size length(DataList), containing subject specific time courses
Loss	loss function value of the best start
FinalLossDiff	value of the loss difference between the last two iterations of the algorithm.
IndLoss	a vector with containing the individual loss function values
LossStarts	loss function values of all starts
Iterations	Number of iterations
starts	dataframe with the used starting partitions

Author(s)

Jeffrey Durieux

Examples

## Not run: 
CICA_data <- Sim_CICA(Nr = 15, Q = 5, R = 4, voxels = 100, timepoints = 10,
E = 0.4, overlap = .25, externalscore = TRUE)

multiple_output = CICA(DataList = CICA_data$X, nComp = 2:6, nClus = 1:5,
method = 'fastICA',userGrid = NULL, RanStarts = 30, RatStarts = NULL, 
pseudo = c(0.1, 0.2),pseudoFac = 2, userDef = NULL, scalevalue = 1000, 
center = TRUE,maxiter = 100, verbose = TRUE, ctol = .000001)

summary(multiple_output$Q_5_R_4)

plot(multiple_output$Q_5_R_4)

## End(Not run)

CICA documentation built on Sept. 11, 2024, 6:33 p.m.