Package 'CICA' reference manual

Title:	Clusterwise Independent Component Analysis
Description:	Clustering multi-subject resting state functional Magnetic Resonance Imaging data. This methods enables the clustering of subjects based on multi-subject resting state functional Magnetic Resonance Imaging data. Objects are clustered based on similarities and differences in cluster-specific estimated components obtained by Independent Component Analysis.
Authors:	Jeffrey Durieux [aut, cre], Tom Wilderjans [aut], Juan Claramunt Gonzalez [ctb]
Maintainer:	Jeffrey Durieux <[email protected]>
License:	GPL-3
Version:	1.1.1
Built:	2025-03-04 06:19:51 UTC
Source:	https://github.com/jeffreydurieux/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
)
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)

## 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)

Compute modified RV matrix

Description

This function computes a NxN modified RV matrix

Usage

computeRVmat(DataList = DataList, dist = TRUE, verbose = TRUE)
computeRVmat(DataList = DataList, dist = TRUE, verbose = TRUE)

Arguments

`DataList`	a list with matrices
`dist`	boolean if TRUE distance object is returned
`verbose`	boolean if TRUE progressbar is printed to the console

Value

RVsS

a square similarity matrix of class matrix or distance object of class dist containing the pairwise modified RV values

Examples


## Not run: 
CICA_data <- Sim_CICA(Nr = 15, Q = 5, R = 4, voxels = 100, timepoints = 10,
E = 0.4, overlap = .25, externalscore = TRUE)
#Compute single subject ICAs (nClus equals length(ExampleData))
output <- CICA(DataList = CICA_data$X, nStarts = 1,
               nComp = 5, nClus = 9, verbose = FALSE)

RV <- computeRVmat(DataList = output$Sr, dist = TRUE,
                    verbose = FALSE)

# apply hierarchical clustering on RV output
hcl <- hclust(RV)
plot(hcl)

# low dimensional visualisation using Classical Multidimensional Scaling
mds <- cmdscale(RV)
plot(mds)

## End(Not run)

## Not run: 
CICA_data <- Sim_CICA(Nr = 15, Q = 5, R = 4, voxels = 100, timepoints = 10,
E = 0.4, overlap = .25, externalscore = TRUE)
#Compute single subject ICAs (nClus equals length(ExampleData))
output <- CICA(DataList = CICA_data$X, nStarts = 1,
               nComp = 5, nClus = 9, verbose = FALSE)

RV <- computeRVmat(DataList = output$Sr, dist = TRUE,
                    verbose = FALSE)

# apply hierarchical clustering on RV output
hcl <- hclust(RV)
plot(hcl)

# low dimensional visualisation using Classical Multidimensional Scaling
mds <- cmdscale(RV)
plot(mds)

## End(Not run)

Plot method for rstarts object

Description

Two step clustering for finding rational start partitions

Usage

FindRationalStarts(
  DataList,
  RatStarts = "all",
  nComp,
  nClus,
  scalevalue = NULL,
  center = TRUE,
  verbose = TRUE,
  pseudo = NULL,
  pseudoFac = NULL
)

## S3 method for class 'rstarts'
plot(x, type = 1, mdsdim = 2, nClus = NULL, ...)
FindRationalStarts(
  DataList,
  RatStarts = "all",
  nComp,
  nClus,
  scalevalue = NULL,
  center = TRUE,
  verbose = TRUE,
  pseudo = NULL,
  pseudoFac = NULL
)

## S3 method for class 'rstarts'
plot(x, type = 1, mdsdim = 2, nClus = NULL, ...)

Arguments

`DataList`	a list of matrices
`RatStarts`	type of rational start. 'all' computes all types of hclust methods
`nComp`	number of ICA components to extract
`nClus`	Number of clusters for rectangles in dendrogram, default NULL is based on number of clusters present in the object
`scalevalue`	scale each matrix to have an equal sum of squares
`center`	mean center matrices
`verbose`	print output to console
`pseudo`	percentage value for perturbating rational starts to obtain pseudo rational starts
`pseudoFac`	how many pseudo starts per rational start
`x`	an object of `class` rstarts
`type`	type of plot, 1 for a dendrogram, 2 for a multidimensional scaling configuration
`mdsdim`	2 for two dimensional mds configuration, 3 for a three dimensional configuration
`...`	optional arguments passed to `hclust` function

Value

dataframe with (pseudo-) rational and dist object based on the pairwise modified RV values

References

Durieux, J., & Wilderjans, T. F. (2019). Partitioning subjects based on high-dimensional fMRI data: comparison of several clustering methods and studying the influence of ICA data reduction in big data. Behaviormetrika, 46(2), 271-311.

Examples

## Not run: 
CICA_data <- Sim_CICA(Nr = 15, Q = 5, R = 4, voxels = 100, timepoints = 10,
E = 0.4, overlap = .25, externalscore = TRUE)
rats <- FindRationalStarts(DataList = CICA_data$X, nComp = 5, nClus = 4,verbose = TRUE, pseudo = .2)
plot(rats, type = 1, method = 'ward.D2')
plot(rats, type = 2, method = 'ward.D2')
plot(rats, type = 2, method = 'ward.D2', mdsdim = 3)

## End(Not run)


## Not run: 
CICA_data <- Sim_CICA(Nr = 15, Q = 5, R = 4, voxels = 100, timepoints = 10,
E = 0.4, overlap = .25, externalscore = TRUE)
Out_starts <- FindRationalStarts(DataList = CICA_data$X,nComp = 5,nClus = 4,scalevalue = 1000)
plot(Out_starts)
plot(Out_starts, type = 2)
plot(Out_starts, type = 2,mdsdim = 3, method = 'ward.D2')

## End(Not run)


## Not run: 
CICA_data <- Sim_CICA(Nr = 15, Q = 5, R = 4, voxels = 100, timepoints = 10,
E = 0.4, overlap = .25, externalscore = TRUE)
rats <- FindRationalStarts(DataList = CICA_data$X, nComp = 5, nClus = 4,verbose = TRUE, pseudo = .2)
plot(rats, type = 1, method = 'ward.D2')
plot(rats, type = 2, method = 'ward.D2')
plot(rats, type = 2, method = 'ward.D2', mdsdim = 3)

## End(Not run)


## Not run: 
CICA_data <- Sim_CICA(Nr = 15, Q = 5, R = 4, voxels = 100, timepoints = 10,
E = 0.4, overlap = .25, externalscore = TRUE)
Out_starts <- FindRationalStarts(DataList = CICA_data$X,nComp = 5,nClus = 4,scalevalue = 1000)
plot(Out_starts)
plot(Out_starts, type = 2)
plot(Out_starts, type = 2,mdsdim = 3, method = 'ward.D2')

## End(Not run)

Generate random starts

Description

Generate random starts

Usage

GenRanStarts(
  RanStarts,
  nClus,
  nBlocks,
  ARIlim = 0.2,
  itmax = 1000,
  verbose = FALSE
)
GenRanStarts(
  RanStarts,
  nClus,
  nBlocks,
  ARIlim = 0.2,
  itmax = 1000,
  verbose = FALSE
)

Arguments

`RanStarts`	number of randomstarts to generate
`nClus`	number of clusters
`nBlocks`	number of objects
`ARIlim`	maximal value of adjusted Rand Index
`itmax`	maximum number of iterations used to find suitable random starts
`verbose`	boolean that indicates whether the output should be printed on the console

Value

a list where the first element is a matrix with random starts, second element all pairwise ARIs #'

Title

Description

Title

Usage

GenRatStarts(
  DataList,
  RatStarts,
  nComp,
  nClus,
  scalevalue,
  center,
  verbose,
  pseudo,
  pseudoFac
)
GenRatStarts(
  DataList,
  RatStarts,
  nComp,
  nClus,
  scalevalue,
  center,
  verbose,
  pseudo,
  pseudoFac
)

Arguments

`DataList`	DataList
`RatStarts`	Type of rational start
`nComp`	number of components
`nClus`	number of clusters
`scalevalue`	value for blockscaling procedure
`center`	center
`verbose`	verbose
`pseudo`	percentage used for perturbation rational starts (between 0)
`pseudoFac`	multiplication factor for pseudo rational starts

Value

out

Get Papaya Version

Description

Reads the papaya.js file installed and determines version and build

Usage

get_papaya_version()
get_papaya_version()

Value

List of build and version, both characters

Load Nifti files from directory

Description

Load Nifti files from directory

Usage

loadNIfTIs(dir, toMatrix = TRUE)
loadNIfTIs(dir, toMatrix = TRUE)

Arguments

`dir`	Input directory containing nifti files
`toMatrix`	logical if TRUE nifti's are converted to matrices

Value

list object containing Voxel by Time course matrices

Examples

## Not run: 
nifs <- loadNIfTIs('<FolderPath>', toMatrix = T)
outnif <- CICA(DataList = nifs, RanStarts = 2, nComp = 10, nClus = 2)

## End(Not run)

## Not run: 
nifs <- loadNIfTIs('<FolderPath>', toMatrix = T)
outnif <- CICA(DataList = nifs, RanStarts = 2, nComp = 10, nClus = 2)

## End(Not run)

Match components between cluster specific spatial maps

Description

Match components between cluster specific spatial maps

Usage

matcher(x, reference, RV = FALSE, ...)
matcher(x, reference, RV = FALSE, ...)

Arguments

`x`	object of class CICA
`reference`	integer cluster index that serves as the reference. If nifti path is supplied, clusters will be matched to this template
`RV`	compute modified-RV between cluster components
`...`	other arguments

Value

out

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,
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)

matcher(multiple_output$Q_5_R_4, reference = 1, RV = TRUE)

## End(Not run)

## 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,
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)

matcher(multiple_output$Q_5_R_4, reference = 1, RV = TRUE)

## End(Not run)

Match components between cluster specific spatial maps

Description

Match components between cluster specific spatial maps

Usage

## S3 method for class 'CICA'
matcher(x, reference = 1, RV = FALSE, ...)
## S3 method for class 'CICA'
matcher(x, reference = 1, RV = FALSE, ...)

Arguments

`x`	object of class CICA
`reference`	integer cluster index that serves as the reference. If nifti path is supplied, clusters will be matched to this template
`RV`	compute modified-RV between cluster components
`...`	other arguments

Value

out

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,
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)

matcher(multiple_output$Q_5_R_4, reference = 1, RV = TRUE)

## End(Not run)

## 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,
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)

matcher(multiple_output$Q_5_R_4, reference = 1, RV = TRUE)

## End(Not run)

Moore Penrose inverse

Description

Moore Penrose inverse

Usage

mpinv(X)
mpinv(X)

Arguments

`X`	input matrix

Value

mp Moore Penrose inverse of matrix X

View images with Papaya

Description

Writes temporary images out from nifti objects or passes character filenames of images to papaya JS viewer

Usage

papaya(images, outdir = NULL, ...)
papaya(images, outdir = NULL, ...)

Arguments

`images`	character filenames or `nifti` objects to be viewed
`outdir`	output directory for index and all to go
`...`	Options to be passed to `pass_papaya`

Value

Output directory where index.html, js, and copied nii.gz files

Examples

## Not run: 
library(neurobase)
x = nifti(img = array(rnorm(100^3), dim= rep(100, 3)), dim=rep(100, 3), datatype=16)
thresh = datatyper(x > 1)
index.file = papaya(list(x, thresh))

## End(Not run)
## Not run: 
library(neurobase)
x = nifti(img = array(rnorm(100^3), dim= rep(100, 3)), dim=rep(100, 3), datatype=16)
thresh = datatyper(x > 1)
index.file = papaya(list(x, thresh))

## End(Not run)

Papaya Div element output

Description

Get the necessary div output for embedding a papaya image

Usage

papaya_div()
papaya_div()

Value

Character string

Examples

papaya_div()
papaya_div()

View images with Papaya

Description

Writes temporary images out from nifti objects or passes character filenames of images to papaya JS viewer

Usage

pass_papaya(
  L = NULL,
  outdir = NULL,
  daemon = FALSE,
  close_on_exit = TRUE,
  sleeper = 3,
  version = "0.8",
  build = "982"
)
pass_papaya(
  L = NULL,
  outdir = NULL,
  daemon = FALSE,
  close_on_exit = TRUE,
  sleeper = 3,
  version = "0.8",
  build = "982"
)

Arguments

`L`	list of arguments passed to papaya using params
`outdir`	output directory for index and all to go
`daemon`	Argument passed to `server_config`
`close_on_exit`	Should the server close once the function finishes?
`sleeper`	Time in seconds to sleep if `close_on_exit = TRUE`. This allows the server to start up.
`version`	Version of papaya.js and papaya.css to use
`build`	Build of papaya.js and papaya.css to use

Plot method for CICA

Description

Plot method for CICA. This function shows the cluster specific independent components in an interactive viewer using the papayar package

Usage

## S3 method for class 'CICA'
plot(x, brain = "auto", cluster = 1, ...)
## S3 method for class 'CICA'
plot(x, brain = "auto", cluster = 1, ...)

Arguments

`x`	Object of `class` CICA
`brain`	auto
`cluster`	Components of cluster to plot. Only used when non fMRI related data is used
`...`	other arguments

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,
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)

plot(multiple_output$Q_5_R_4, cluster = 2)

## End(Not run)

## 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,
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)

plot(multiple_output$Q_5_R_4, cluster = 2)

## End(Not run)

Plot method for sequential model selection

Description

Plot method for the sequential model selection option for CICA

Usage

## S3 method for class 'ModSel'
plot(x, ...)
## S3 method for class 'ModSel'
plot(x, ...)

Arguments

`x`	Object of `class` ModSel
`...`	other arguments

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,
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)

ModSelOutput <- SequentialScree(multiple_output)

plot(ModSelOutput)

## End(Not run)

## 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,
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)

ModSelOutput <- SequentialScree(multiple_output)

plot(ModSelOutput)

## End(Not run)

Sequential Model Selection for Multiple CICA model

Description

Sequential Model Selection for Multiple CICA model

Usage

SequentialScree(x)
SequentialScree(x)

Arguments

`x`	an object of class MultipleCICA

Value

a list object

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,
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)

ModSelOutput <- SequentialScree(multiple_output)

plot(ModSelOutput)

## End(Not run)

## 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,
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)

ModSelOutput <- SequentialScree(multiple_output)

plot(ModSelOutput)

## End(Not run)

Simulate CICA data

Description

Simulate CICA data

Usage

Sim_CICA(
  Nr,
  Q,
  R,
  voxels,
  timepoints,
  E,
  overlap = NULL,
  externalscore = FALSE
)
Sim_CICA(
  Nr,
  Q,
  R,
  voxels,
  timepoints,
  E,
  overlap = NULL,
  externalscore = FALSE
)

Arguments

`Nr`	number of subjects per cluster
`Q`	number of components
`R`	number of clusters
`voxels`	number of voxels
`timepoints`	number of time points
`E`	proportion of independent gaussian noise
`overlap`	amount of overlap between S across clusters. Smaller value means more overlap
`externalscore`	add simulated external score (default is FALSE)

Value

a list with simulated CICA data

Examples

## Not run: 

#Use set.seed(1) to obtain the dataset used in the article "Clusterwise
#Independent Component Analysis (CICA): an R package for clustering subjects
#based on ICA patterns underlying three-way (brain) data"

Xe <- Sim_CICA(Nr = 15, Q = 5, R = 4, voxels = 100, timepoints = 10,
E = 0.4, overlap = .25, externalscore = TRUE)

## End(Not run)

## Not run: 

#Use set.seed(1) to obtain the dataset used in the article "Clusterwise
#Independent Component Analysis (CICA): an R package for clustering subjects
#based on ICA patterns underlying three-way (brain) data"

Xe <- Sim_CICA(Nr = 15, Q = 5, R = 4, voxels = 100, timepoints = 10,
E = 0.4, overlap = .25, externalscore = TRUE)

## End(Not run)

Convert Cluster specific independent components to NIFTI format

Description

Convert Cluster specific independent components to NIFTI format

Usage

Sr_to_nifti(x, write = FALSE, ...)
Sr_to_nifti(x, write = FALSE, ...)

Arguments

`x`	an object of `class` CICA
`write`	if TRUE, NIfTI files are written to current working directory
`...`	other arguments passed to RNifti::writeNifti

Value

a list with niftiImage files

Examples

## Not run: 
nifs <- loadNIfTIs('<FolderPath>', toMatrix = T)
outnif <- CICA(DataList = nifs, RanStarts = 2, nComp = 10, nClus = 2)
test <- Sr_to_nifti(outnif,write = T, datatype = 'int16', version = 2)

## End(Not run)

## Not run: 
nifs <- loadNIfTIs('<FolderPath>', toMatrix = T)
outnif <- CICA(DataList = nifs, RanStarts = 2, nComp = 10, nClus = 2)
test <- Sr_to_nifti(outnif,write = T, datatype = 'int16', version = 2)

## End(Not run)

Summary method for class CICA

Description

Summarize a CICA analysis

Usage

## S3 method for class 'CICA'
summary(object, ...)
## S3 method for class 'CICA'
summary(object, ...)

Arguments

`object`	Object of the type produced by `CICA`
`...`	Additional arguments

Value

summary.CICA returns an overview of the estimated clustering of a CICA analysis

`PM`	Partitioning matrix
`tab`	tabulation of the clustering
`Loss`	Loss function value of the solution

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,
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)

## End(Not run)


## 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,
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)

## End(Not run)

Summary method for class MultipleCICA

Description

Summarize a CICA analysis

Usage

## S3 method for class 'MultipleCICA'
summary(object, ...)
## S3 method for class 'MultipleCICA'
summary(object, ...)

Arguments

`object`	Object of the type produced by `CICA`
`...`	Additional arguments

Value

summary.MultipleCICA returns an overview of the estimated clustering of a CICA analysis

`PM`	Partitioning matrix
`tab`	tabulation of the clustering
`Loss`	Loss function value of the solution

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,
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)

## End(Not run)

## 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,
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)

## End(Not run)

Update Papaya build version from GitHub

Description

Updates the papaya version in the papayar package to the most current on GitHub

Usage

update_papaya_build(
  type = c("standard", "minimal", "nodicom", "nojquery", "standard-with-atlas-local",
    "standard-with-atlas"),
  verbose = TRUE
)
update_papaya_build(
  type = c("standard", "minimal", "nodicom", "nojquery", "standard-with-atlas-local",
    "standard-with-atlas"),
  verbose = TRUE
)

Arguments

`type`	Type of release. Standard is default
`verbose`	Should download progress be shown?

Value

Result of get_papaya_version after downloading

Package 'CICA'

Help Index

CICA: Clusterwise Independent Component Analysis

Description

Usage

Arguments

Value

Author(s)

Examples

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Description

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Arguments

Value

Examples

Embed images with Papaya

Description

Usage

Arguments

Value

Plot method for rstarts object

Description

Usage

Arguments

Value

References

Examples

Generate random starts

Description

Usage

Arguments

Value

Title

Description

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Arguments

Value

Get Papaya Version

Description

Usage

Value

Load Nifti files from directory

Description

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Arguments

Value

Examples

Match components between cluster specific spatial maps

Description

Usage

Arguments

Value

Examples

Match components between cluster specific spatial maps

Description

Usage

Arguments

Value

Examples

Moore Penrose inverse

Description

Usage

Arguments

Value

View images with Papaya

Description

Usage

Arguments

Value

Examples

Papaya Div element output

Description

Usage

Value

Examples

View images with Papaya

Description

Usage

Arguments

Plot method for CICA