pls: Positively constrained least squares

In cols: Constrained Ordinary Least Squares

Positively constrained least squares

Description

Positively constrained least squares.

Usage

pls(y, x)
mpls(y, x)

Arguments

y	The response variable. For the pls() a numerical vector with observations, but for the mpls() a numerical matrix .
x	A matrix with independent variables, the design matrix.

Details

The constraint is that all beta coefficients (including the constant) are non negative, i.e. min \sum_{i=1}^n(y_i-\bm{x}_i^\top\bm{\beta})^2 such that \beta_j \geq 0. The pls() function performs a single regression model, whereas the mpls() function performs a regression for each column of y. Each regression is independent of the others.

Value

A list including:

be	A numerical matrix with the positively constrained beta coefficients.
mse	A numerical vector with the mean squared error(s).

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

See Also

cls, pcls, mvpls

Examples

x <- as.matrix( iris[1:50, 1:4] )
y <- rnorm(50)
pls(y, x)

cols documentation built on April 3, 2025, 10:33 p.m.