jointICA: Joint decomposition with Independent Component Analysis

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

Description

Joint decomposition of several linked matrices with Independent Component Analysis (ICA)

Usage

jointICA(
  dataset,
  group,
  comp_num,
  weighting = NULL,
  max_ite = 100,
  max_err = 1e-04,
  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
comp_num	A vector indicates the dimension of each compoent
weighting	Weighting of each dataset, initialized to be NULL
max_ite	The maximum number of iterations for the jointPCA algorithms to run, default value is set to 100
max_err	The maximum error of loss between two iterations, or the program will terminate and return, default value is set to be 0.0001
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 jointPCA 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))
proj_dataset = list(matrix(runif(5000, 1, 2), nrow = 100, ncol = 50))
proj_group = list(c(TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE))
comp_num = c(2,2,2,2,2,2,2,2,2)
res_jointICA = jointICA(dataset, group, comp_num, proj_dataset = proj_dataset, proj_group = proj_group)

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