seedpls: Partial least squares through iterative projections

In seedCCA: Seeded Canonical Correlation Analysis

Partial least squares through iterative projections

Description

Returns partial least squares estimates through iterative projections. And, the function results in subclass "seedpls".

Usage

seedpls(X, Y, u=5, scale=FALSE)

Arguments

X	numeric matrix (n * p), a set of predictors
Y	numeric vector or matrix (n * r), responses (it can be multi-dimensional)
u	numeric, the number of projections. The default is 5.
scale	logical, FALSE is default. If TRUE, each predictor is standardized with mean 0 and variance 1

Value

coef	the estimated coefficients for each iterative projection upto u
u	the maximum number of projections
X	Predictors
Y	Response
scale	status of scaling predictors

Examples

########  data(cookie) ########
data(cookie)
myseq<-seq(141,651,by=2)
X<-as.matrix(cookie[-c(23,61),myseq])
Y<-as.matrix(cookie[-c(23,61),701:704])

fit.pls1 <- seedpls(X,Y[,1]) ## one-dimensional response
fit.pls2 <- seedpls(X,Y, u=6, scale=TRUE) ## four-dimensional response

seedCCA documentation built on June 9, 2022, 9:05 a.m.