twoStageiLCA.rank: Two-staged Independent LCA and automatic rank selection

In CHuanSite/PJD: Partial and Joint Decomposiiton(PJD)

Description

Two-staged decomposition of several matrices with Independent LCA, twoStageLCA is first performed on the data, the rank selection procedure is automatic based on BEMA. Then, fastICA is implemented on the score to extract the independent components.

Usage

twoStageiLCA.rank(
  dataset,
  group,
  weighting = NULL,
  total_number = NULL,
  threshold,
  backup = 0,
  plotting = FALSE,
  proj_dataset = NULL,
  proj_group = NULL
)

Arguments

dataset	A list of dataset to be analyzed
group	A list of grouping of the datasets, indicating the relationship between datasets
weighting	Weighting of each dataset, initialized to be NULL
total_number	Total number of components will be extracted, if default value is set to NA, then BEMA will be used.
threshold	The threshold used to cutoff the eigenvalues
backup	A backup variable, which permits the overselection of the components by BEMA
plotting	A boolean value to determine whether to plot the scree plot or not, default to be False
proj_dataset	The datasets to be projected on
proj_group	The grouping of projected data sets

Value

A list contains the component and the score of each dataset on every component after twoStageiLCA.rank algorithm

Examples

dataset = list(matrix(runif(5000, 1, 2), nrow = 100, ncol = 50),
matrix(runif(5000, 1, 2), nrow = 100, ncol = 50),
matrix(runif(5000, 1, 2), nrow = 100, ncol = 50),
matrix(runif(5000, 1, 2), nrow = 100, ncol = 50))
group = list(c(1, 2, 3, 4), c(1, 2), c(3, 4), c(1, 3), c(2, 4), c(1), c(2), c(3), c(4))
threshold = c(3, 1.5, 1.5, 1.5, 1.5, 0.5, 0.5, 0.5, 0.5)
res_twoStageiLCA.rank = twoStageiLCA.rank(dataset, group, threshold = threshold)

CHuanSite/PJD documentation built on Oct. 26, 2021, 1 p.m.