ols.compcomp: Constrained linear least squares for compositional responses...

In Compositional: Compositional Data Analysis

Constrained linear least squares for compositional responses and predictors

Description

Constrained linear least squares for compositional responses and predictors.

Usage

ols.compcomp(y, x, xnew = NULL)

Arguments

y	A matrix with the compositional data (dependent variable). Zero values are allowed.
x	A matrix with the compositional predictors. Zero values are allowed.
xnew	If you have new data use it, otherwise leave it NULL.

Details

The function performs least squares regression where the beta coefficients are constained to be positive and sum to 1. We were inspired by the transformation-free linear regression for compositional responses and predictors of Fiksel, Zeger and Datta (2020).

Value

A list including:

runtime	The time required by the regression.
mse	The mean squared errors.
be	The beta coefficients.
est	The fitted of xnew if xnew is not NULL.

Author(s)

Michail Tsagris.

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

References

Jacob Fiksel, Scott Zeger and Abhirup Datta (2020). A transformation-free linear regression for compositional outcomes and predictors. https://arxiv.org/pdf/2004.07881.pdf

See Also

cv.olscompcomp, tflr, kl.alfapcr

Examples

library(MASS)
set.seed(1234)
y <- rdiri(214, runif(4, 1, 3))
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
mod <- ols.compcomp(y, x)
mod

Compositional documentation built on Oct. 23, 2023, 5:09 p.m.