| The SCLS model {Compositional} | R Documentation |
Simplicial constrained linear least squares (SCLS) for compositional responses and predictors
Description
Simplicial constrained linear least squares (SCLS) for compositional responses and predictors.
Usage
scls(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). Our implementation now uses quadratic programming instead of the function optim, and the solution is more accurate and extremely fast.
Value
A list including:
mse |
The mean squared error. |
be |
The beta coefficients. |
est |
The fitted of xnew if xnew is not NULL. |
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Tsagris. M. (2024). Constrained least squares simplicial-simplicial regression. https://arxiv.org/pdf/2403.19835.pdf
See Also
Examples
library(MASS)
set.seed(1234)
y <- rdiri(214, runif(4, 1, 3))
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
mod <- scls(y, x)
mod