DISCOsca: DISCO-SCA rotation.

In khliland/multiblock: Multiblock Data Fusion in Statistics and Machine Learning

DISCO-SCA rotation.

Description

A DISCO-SCA procedure for identifying common and distinctive components. The code is adapted from the orphaned RegularizedSCA package by Zhengguo Gu.

Usage

DISCOsca(DATA, R, Jk)

Arguments

DATA	A matrix, which contains the concatenated data with the same subjects from multiple blocks. Note that each row represents a subject.
R	Number of components (R>=2).
Jk	A vector containing number of variables in the concatenated data matrix.

Value

Trot_best	Estimated component score matrix (i.e., T)
Prot_best	Estimated component loading matrix (i.e., P)
comdist	A matrix representing common distinctive components. (Rows are data blocks and columns are components.) 0 in the matrix indicating that the corresponding component of that block is estimated to be zeros, and 1 indicates that (at least one component loading in) the corresponding component of that block is not zero. Thus, if a column in the comdist matrix contains only 1's, then this column is a common component, otherwise distinctive component.
propExp_component	Proportion of variance per component.

References

Schouteden, M., Van Deun, K., Wilderjans, T. F., & Van Mechelen, I. (2014). Performing DISCO-SCA to search for distinctive and common information in linked data. Behavior research methods, 46(2), 576-587.

Examples

## Not run: 
DATA1 <- matrix(rnorm(50), nrow=5)
DATA2 <- matrix(rnorm(100), nrow=5) 
DATA <- cbind(DATA1, DATA2)
R <- 5
Jk <- c(10, 20) 
DISCOsca(DATA, R, Jk)

## End(Not run)

khliland/multiblock documentation built on April 17, 2025, 5:09 p.m.