AclustsCCA.cont: Implement additional permutations of AclustsCCA

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

Implement additional permutations of AclustsCCA

Description

Implement an iterative penalized least squares approach to sparse canonical correlation analysis (SparseCCA) with various penalty functions.

Usage

AclustsCCA.cont(obj, X, Y, maxnum = NULL, maxB = 10000)

Arguments

obj	A result of AclustsCCA function.
X	n by p exposure data matrix, where n is sample size and p is number of exposures.
Y	n by q outcome data matrix, where n is sample size and q is number of outcomes.
maxnum	A maximal total number of permutations across all the clusters.
maxB	A maximal number of permutations for a single cluster.

Value

The function returns a list of 6 objects according to the following order:

• clusters.list : A list of clusters with CpG sites obtained using A-clustering, each item is a cluster that contains a set of probes. If A-clustering is not implemented inside AclustsCCA, return NA.

• ALPHA.observed : A list of estimated canonical vector of length p corresponding to the exposure data X for each cluster.

• BETA.observed : A list of estimated canonical vector of length q corresponding to the outcome data Y for each cluster.

• cancors.observed : A vector of estimated canonical correlation for each cluster.

• permutation.result : A mmctest object that contains permutation results.

• settings : A settings used for the analysis.

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