sNPLS (version 0.1.3)

sNPLS: Fit a sNPLS model

Description

Fits a N-PLS regression model imposing a L1 penalization on wj and wk matrices

Usage

sNPLS(XN, Y, ncomp = 2, conver = 1e-16, max.iteration = 10000, keepJ = rep(ncol(XN), ncomp), keepK = rep(rev(dim(XN))[1], ncomp), silent = F)

Arguments

XN

A three-way array containing the predictors.

Y

A matrix containing the response.

ncomp

Number of components in the projection

conver

Convergence criterion

max.iteration

Maximum number of iterations

keepJ

Number of variables to keep for each component

keepK

Number of 'times' to keep for each component

silent

Show output?

Value

A fitted sNPLS model

References

C. A. Andersson and R. Bro. The N-way Toolbox for MATLAB Chemometrics & Intelligent Laboratory Systems. 52 (1):1-4, 2000.

Shen, H. and Huang, J. Z. (2008). Sparse principal component analysis via regularized low rank matrix approximation. Journal of Multivariate Analysis 99, 1015-1034

Examples

X_npls<-array(rpois(7500, 10), dim=c(50, 50, 3))

Y_npls<-matrix(2+0.4*X_npls[,5,1]+0.7*X_npls[,10,1]-0.9*X_npls[,15,1]+
0.6*X_npls[,20,1]- 0.5*X_npls[,25,1]+rnorm(50), ncol=1)

fit<-sNPLS(X_npls, Y_npls, ncomp=3, keepJ = rep(2,3) , keepK = rep(1,3))