rrs.fit: Fitting reduced-rank ridge regression with given rank and...

In rrpack: Reduced-Rank Regression

Fitting reduced-rank ridge regression with given rank and shrinkage penalty

Description

Fitting reduced-rank ridge regression with given rank and shrinkage penalty

Usage

rrs.fit(Y, X, nrank = min(ncol(Y), ncol(X)), lambda = 1, coefSVD = FALSE)

Arguments

Y	a matrix of response (n by q)
X	a matrix of covariate (n by p)
nrank	an integer specifying the desired rank
lambda	tunging parameter for the ridge penalty
coefSVD	logical indicating the need for SVD for the coeffient matrix int the output

Value

S3 rrr object, a list consisting of

coef	coefficient of rrs
coef.ls	coefficient of least square
fitted	fitted value of rrs
fitted.ls	fitted value of least square
A	right singular matrix
Ad	sigular value vector
nrank	rank of the fitted rrr

References

Mukherjee, A. and Zhu, J. (2011) Reduced rank ridge regression and its kernal extensions.

Mukherjee, A., Chen, K., Wang, N. and Zhu, J. (2015) On the degrees of freedom of reduced-rank estimators in multivariate regression. Biometrika, 102, 457–477.

Examples

library(rrpack)
Y <- matrix(rnorm(400), 100, 4)
X <- matrix(rnorm(800), 100, 8)
rfit <- rrs.fit(Y, X)

rrpack documentation built on June 16, 2022, 9:05 a.m.