FindRationalStarts: Plot method for rstarts object

In CICA: Clusterwise Independent Component Analysis

Plot method for rstarts object

Description

Plot method for rstarts object

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, ...)

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)

CICA documentation built on July 26, 2023, 5:51 p.m.