Sparse.alternating: Penalized least squares function used for SparseCCA

In jennyjyounglee/AclustsCCA: A Cluster Based Sparse Canonical Correlation Analayis to Test Associations Between A Multi-Pollutant Mixture and High-Dimentional DNA Methylation

Penalized least squares function used for SparseCCA

Description

Implement an penalized least squares needed to run sparse canonical correlation analysis (SparseCCA) with various penalty functions. Modified Wilms, Ines, and Christophe Croux. "Robust sparse canonical correlation analysis." BMC systems biology 10.1 (2016): 1-13. The original code is accessible https://sites.google.com/view/iwilms/software?authuser=0

INPUT

Usage

Sparse.alternating(Xreg, Yreg, method, groupidx = NULL)

Arguments

Xreg	: A data matrix of n rows
Yreg	: A vector of length n
Xmethod	: penalty function for the exposure, i.e. penalty function when regressing Yreg onto Xreg. Possible values are: "lasso": Lasso "alasso": Adaptive Lasso "gglasso": Group Lasso "SGL": Sparse Group Lasso "OLS": Ordinary Least Square

Examples

data.list <- generate.data(n=500)
DATA.X <- data.list$DATA.X
DATA.Y <- data.list$DATA.Y
Sparse.alternating.result <- Sparse.alternating(Xreg=DATA.X,Yreg=DATA.Y[,1],method="SGL")
str(Sparse.alternating.result)

jennyjyounglee/AclustsCCA documentation built on June 15, 2022, 7:45 p.m.