seedols: Ordinary least squares

In seedCCA: Seeded Canonical Correlation Analysis

Ordinary least squares

Description

Returns ordinary least squares estimates. And, the function results in subclass "seedols". For this function to work, either X or Y has to be one-dimensional. It is not necessary that X and Y should be predictors and response, respectively. Regardless of the position in the arguments, the one-dimensional and multi-dimensional variables become response and predictors, respectively.

Usage

seedols(X, Y)

Arguments

X	numeric vector or matrix, a first set of variables
Y	numeric vector or matrix, a second set of variables

Value

coef	the estimated coefficients for each iterative projection upto u
X	X, the first set
Y	Y, the second set

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])

ols1 <- seedols(X[,1:4],Y[,1])
ols2 <- seedols(Y[,1],X[,1:4])
## ols1 and ols2 are the same results.

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