| codalm {codalm} | R Documentation |
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)