CICA {CICA}

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)