FindRationalStarts {CICA}

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, ...)

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)