FindRationalStarts {CICA} | R Documentation |
Plot method for rstarts object
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, ...)
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 |
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 |
dataframe with (pseudo-) rational and dist object based on the pairwise modified RV values
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.
## 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)