scca: Sparse canonical covariance analysis

In hierBipartite: Bipartite Graph-Based Hierarchical Clustering

Description

'scca' is used to perform sparse canonical covariance analysis (SCCA)

Usage

scca(X,Y,penalty="HL",lamx=c(1,2,3),lamy=c(1,2,3),nc=1,
tuning="CV.alt",K=5,seed=NULL,center=TRUE,scale=FALSE)

Arguments

X	n-by-p data matrix, where n is the number of subjects and p is the number of variables
Y	n-by-q data matrix, where q is the number of variables
penalty	"HL" is the unbounded penalty proposed by Lee and Oh (2009). "LASSO" (Tibshirani, 1996), "SCAD" (Fan and Li, 2001) and "SOFT" (soft thresholding) are also available as other penalty options. Default is "HL".
lamx	A vector specifying grid points of the tuning parameter for X. Default is (1,2,3).
lamy	A vector specifying grid points of the tuning parameter for Y. Default is (1,2,3).
nc	Number of components (canonical vectors). Default is 1.
tuning	How to find optimal tuning parameters for the sparsity. If tuning="CV.full", then the tuning parameters are selected automatically via K-fold cross-validation by using 2-dim'l grid search. If "CV.alt", then a sequential 1-dim'l search method is applied instead of the 2-dim'l grid search. Default is "CV.alt".
K	Perform K-fold cross-validation.
seed	Seed number for initialization. A random initial point is generated for tuning="CV.alt".
center	The columns of the data matrix are centered to have mean zero. Default is TRUE.
scale	The columns of the data matrix are scaled to have variance 1. Default is FALSE.

Details

Sparse CCA uses a random-effect model approach to obtain sparse regression. This model gives unbounded gains for zero loadings at the origin. Various penalty functions can be adapted as well.

Value

• A: p-by-nc matrix, k-th colum of A corresponds to k-th pattern

• B: q-by-nc matrix, k-th colum of B corresponds to k-th pattern (canonical vector) for Y

• U: n-by-nc matrix. k-th column of U corresponds to k-th score associated with k-th pattern for X

• V: n-by-nc matrix. k-th column of V corresponds to k-th score associated with k-th pattern for Y

• lambda: nc-by-2 matrix. k-th row of lambda corresponds to the optimal tuning parameters for k-th pattern pairs

• CR: average cross-validated sample covariance

Author(s)

Woojoo Lee, Donghwan Lee, Youngjo Lee and Yudi Pawitan

References

Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011) Sparse Canonical Covariance Analysis for High-throughput Data

Examples

## Example 1
## A very simple simulation example
n<-10; p<-50; q<-20
X = matrix(rnorm(n*p),ncol=p)
Y = matrix(rnorm(n*q),ncol=q)
scca(X,Y)

hierBipartite documentation built on Feb. 16, 2021, 5:07 p.m.