tflr2: The TFLR model with multiple compositional predictors

In Compositional: Compositional Data Analysis

The TFLR model with multiple compositional predictors

Description

The TFLR model with multiple compositional predictors

Usage

tflr2(y, x, wei = FALSE, xnew = NULL)

Arguments

y	A matrix with the compositional data (dependent variable). Zero values are allowed.
x	A list of matrices with the compositional predictors. Zero values are allowed.
wei	Do you want weights among the different simplicial predictors? The default is FALSE.
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)}. This is a self implementation of the function that can be found in the package codalm. This function allows for more than one simplicial predictors and offers the possibility of assigning weights to each simplicial predictor.

Value

A list including:

ini.mse	The mean squared error when all simplicial predictors carry equal weight.
ini.be	The beta coefficients when all simplicial predictors carry equal weight.
mse	The mean squared error when the simplicial predictors carry unequal weights.
weights	The weights in a vector form. A vector of length equal to the number of rows of the matrix of coefficients.
am	The vector of weights, one for each simplicia predictor. The length of the vector is equal to the number of simplicial predictors.
est	The fitted of xnew if xnew is not NULL.

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

cv.scls, tflr, scls.indeptest

Examples

library(MASS)
set.seed(1234)
y <- rdiri(214, runif(4, 1, 3))
x1 <- as.matrix(fgl[, 2:9])
x <- list()
x[[ 1 ]] <- x1 / rowSums(x1)
x[[ 2 ]] <- Compositional::rdiri(214, runif(4))
mod <- tflr2(y, x)
mod

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