codalm: Transformation-free Linear Regression for Compositional...

In codalm: Transformation-Free Linear Regression for Compositional Outcomes and Predictors

Description

Implements the expectation-maximization (EM) algorithm as described in Fiksel et al. (2020) for transformation-free linear regression for compositional outcomes and predictors.

Usage

codalm(y, x, accelerate = TRUE)

Arguments

y	A matrix of compositional outcomes. Each row is an observation, and must sum to 1. If any rows do not sum to 1, they will be renormalized
x	A matrix of compositional predictors. Each row is an observation, and must sum to 1. If any rows do not sum to 1, they will be renormalized
accelerate	A logical variable, indicating whether or not to use the Squarem algorithm for acceleration of the EM algorithm. Default is TRUE.

Value

A D_s x D_r compositional coefficient matrix, where D_s and D_r are the dimensions of the compositional predictor and outcome, respectively

References

https://onlinelibrary.wiley.com/doi/full/10.1111/biom.13465

Examples

data("educFM")
father <- as.matrix(educFM[,2:4])
y <- father / rowSums(father)
mother <- as.matrix(educFM[,5:7] )
x <- mother/rowSums(mother)
codalm(y, x)

codalm documentation built on July 27, 2021, 1:07 a.m.