Positively constrained least squares {cols} | R Documentation |
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 mtsagris@uoc.gr.
See Also
Examples
x <- as.matrix( iris[1:50, 1:4] )
y <- rnorm(50)
pls(y, x)