atflr: The alpha-TFLR model for compositional responses and...

In Compositional: Compositional Data Analysis

The \alpha-TFLR model for compositional responses and predictors

Description

The \alpha-TFLR model for compositional responses and predictors.

Usage

atflr(y, x, a = seq(0.1, 1, by = 0.1), xnew)

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.
a	A vector or a single number of values of the \alpha-parameter. This has to be different from zero, and it can take negative values if there are no zeros in the simplicial response (y).
xnew	The new data for which predictions will be made.

Details

This is an extension of the TFLR model that includes the \alpha-transformation and is intended solely for prediction purposes.

Value

A list with matrices containing the predicted simplicial response values, one matrix for each value of \alpha.

Author(s)

Michail Tsagris.

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

References

Fiksel J., Zeger S. and Datta A. (2022). A transformation-free linear regression for compositional outcomes and predictors. Biometrics, 78(3): 974–987.

Tsagris. M. (2024). Constrained least squares simplicial-simplicial regression. https://arxiv.org/pdf/2403.19835.pdf

See Also

tflr, cv.atflr, ascls

Examples

library(MASS)
set.seed(1234)
y <- rdiri(214, runif(4, 1, 3))
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
mod <- ascls(y, x, a = c(0.5, 1), xnew = x)
mod

Compositional documentation built on Oct. 9, 2024, 5:10 p.m.