tflr: Transformation-free linear regression for compositional...

In Compositional: Compositional Data Analysis

Transformation-free linear regression for compositional responses and predictors

Description

Transformation-free linear regression for compositional responses and predictors.

Usage

tflr(y, x, xnew = NULL)

Arguments

y	A matrix with the compositional response. 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 transformation-free linear regression for compositional responses and predictors is implemented. The function to be minized is -\sum_{i=1}^ny_i\log{y_i/(X_iB)}.

Value

A list including:

runtime	The time required by the regression.
loglik	The log-likelihood.
be	The beta coefficients.
est	The fitted values 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.tflr, ols.compcomp kl.alfapcr

Examples

library(MASS)
y <- rdiri(214, runif(3, 1, 3))
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
mod <- tflr(y, x, x)
mod

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