LSfitNonNeg: Non-negative regression fits with a sparse overparameterized...

In SSBtools: Statistics Norway's Miscellaneous Tools

Non-negative regression fits with a sparse overparameterized model matrix

Description

Assuming z = t(x) %*% y + noise, a non-negatively modified least squares estimate of t(x) %*% y is made.

Usage

LSfitNonNeg(x, z, limit = 1e-10, viaQR = FALSE, printInc = TRUE)

Arguments

x	A matrix
z	A single column matrix
limit	Lower limit for non-zero fits. Set to NULL or -Inf to avoid the non-zero restriction.
viaQR	Least squares fits obtained using qr when TRUE.
printInc	Printing "..." to console when TRUE.

Details

The problem is first reduced by elimination some rows of x (elements of y) using GaussIndependent. Thereafter least squares fits are obtained using solve or qr. Possible negative fits will be forced to zero in the next estimation iteration(s).

Value

A fitted version of z

Author(s)

Øyvind Langsrud

Examples

set.seed(123)
data2 <- SSBtoolsData("z2")
x <- ModelMatrix(data2, formula = ~fylke + kostragr * hovedint - 1)
z <- t(x) %*% data2$ant + rnorm(ncol(x), sd = 3)
LSfitNonNeg(x, z)
LSfitNonNeg(x, z, limit = NULL)

## Not run: 
mf <- ~region*mnd + hovedint*mnd + fylke*hovedint*mnd + kostragr*hovedint*mnd
data4 <- SSBtoolsData("sosialFiktiv")
x <- ModelMatrix(data4, formula = mf)
z <- t(x) %*% data4$ant + rnorm(ncol(x), sd = 3)
zFit <- LSfitNonNeg(x, z)

## End(Not run)

SSBtools documentation built on July 9, 2023, 6:16 p.m.