Permutation linear independence test in the TFLR model {Compositional}

Permutation linear independence test in the TFLR model

Description

Permutation linear independence test in the TFLR model.

Usage

tflr.indeptest(y, x, R = 999, ncores = 1)

Arguments

y

A matrix with the compositional data (dependent variable). Zero values are allowed.

x

A matrix with the compositional predictors. Zero values are in general allowed, but there can be cases when these are problematic.

R

The number of permutations to perform.

ncores

The number of cores to use in case you are interested for parallel computations.

Details

Permutation independence test in the constrained linear least squares for compositional responses and predictors is performed. The observed test statistic is the Kullback-Leibler divergence computed by tflr. Then, the rows of X are permuted B times and each time the TFLR is performed and the Kullback-Leibler is computed. The p-value is then computed in the usual way.

Value

The p-value for the test of linear independence between the simplicial response Y and the simplicial predictor X.

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, scls, tflr.betest

Examples

library(MASS)
set.seed(1234)
y <- rdiri(214, runif(4, 1, 3))
x <- as.matrix(fgl[, 2:9])
x <- x / rowSums(x)
tflr.indeptest(y, x, R = 19)