| Type: | Package |
| Title: | Transformation-Free Linear Regression for Compositional Outcomes and Predictors |
| Version: | 0.1.3 |
| Maintainer: | Sandipan Pramanik <sandy.pramanik@gmail.com> |
| Description: | Implements the expectation-maximization (EM) algorithm as described in Fiksel et al. (2022) <doi:10.1111/biom.13465> for transformation-free linear regression for compositional outcomes and predictors. |
| License: | GPL-2 |
| Imports: | SQUAREM (≥ 2020.3), future, future.apply |
| Encoding: | UTF-8 |
| URL: | https://github.com/jfiksel/codalm |
| BugReports: | https://github.com/jfiksel/codalm/issues |
| RoxygenNote: | 7.3.3 |
| Suggests: | knitr, gtools, remotes, testthat, rmarkdown, ggtern, ggplot2 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-12-11 11:16:42 UTC; sandipanpramanik |
| Author: | Jacob Fiksel 
[aut],
Abhirup Datta [ctb],
Sandipan Pramanik
[cre, ctb] |
| Repository: | CRAN |
| Date/Publication: | 2025-12-11 16:20:08 UTC |
Transformation-free Linear Regression for Compositional Outcomes and Predictors
Description
Implements the expectation-maximization (EM) algorithm as described in Fiksel et al. (2022) 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
Examples
require(ggtern)
data("WhiteCells", package = 'ggtern')
image <- subset(WhiteCells, Experiment == "ImageAnalysis")
image_mat <- as.matrix(image[,c("G", "L", "M")])
microscopic <- subset(WhiteCells, Experiment == "MicroscopicInspection")
microscopic_mat <- as.matrix(microscopic[,c("G", "L", "M")])
x <- image_mat / rowSums(image_mat)
y <- microscopic_mat / rowSums(microscopic_mat)
codalm(y, x)
Bootstrap Confidence Intervals Linear Regression for Compositional Outcomes and Predictors
Description
Implements percentile based bootstrapping to estimate the confidence intervals for the regression coefficients when doing linear regression for compositional outcomes and predictors
Usage
codalm_ci(
y,
x,
accelerate = TRUE,
nboot = 500,
conf = 0.95,
parallel = FALSE,
ncpus = NULL,
strategy = NULL,
init.seed = 123
)
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 |
nboot |
The number of bootstrap repetitions to use. Default is 500 |
conf |
A scalar between 0 and 1 containing the confidence level of the required intervals. Default is .95. |
parallel |
A logical variable, indicating whether or not to use a parallel operation for computing the permutation statistics |
ncpus |
Optional argument. When provided, is an integer giving the number of clusters to be used in parallelization. Defaults to the number of cores, minus 1. |
strategy |
Optional argument. When provided, this will be the evaluation function
(or name of it) to use for parallel computation (if parallel = TRUE). Otherwise,
if parallel = TRUE, then this will default to multisession. See |
init.seed |
The initial seed for the permutations. Default is 123. |
Value
A list, with ci_L and ci_U, giving the lower and upper bounds
of each element of the B matrix
Examples
require(ggtern)
data("WhiteCells", package = 'ggtern')
image <- subset(WhiteCells, Experiment == "ImageAnalysis")
image_mat <- as.matrix(image[,c("G", "L", "M")])
microscopic <- subset(WhiteCells, Experiment == "MicroscopicInspection")
microscopic_mat <- as.matrix(microscopic[,c("G", "L", "M")])
x <- image_mat / rowSums(image_mat)
y <- microscopic_mat / rowSums(microscopic_mat)
codalm_ci(y, x, nboot = 50, conf = .95)
Permutation Test for Linear Independence Between Compositional Outcomes and Predictors
Description
Implements the loss function based permutation test as described in Fiksel et al. (2022) for a test of linear independence between compositional outcomes and predictors.
Usage
codalm_indep_test(
y,
x,
nperms = 500,
accelerate = TRUE,
parallel = FALSE,
ncpus = NULL,
strategy = NULL,
init.seed = 123
)
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 |
nperms |
The number of permutations. Default is 500. |
accelerate |
A logical variable, indicating whether or not to use the Squarem algorithm for acceleration of the EM algorithm. Default is TRUE. |
parallel |
A logical variable, indicating whether or not to use a parallel operation for computing the permutation statistics |
ncpus |
Optional argument. When provided, is an integer giving the number of clusters to be used in parallelization. Defaults to the number of cores, minus 1. |
strategy |
Optional argument. When provided, this will be the evaluation function
(or name of it) to use for parallel computation (if parallel = TRUE). Otherwise,
if parallel = TRUE, then this will default to multisession. See |
init.seed |
The initial seed for the permutations. Default is 123. |
Value
The p-value for the independence test
Examples
require(gtools)
x <- rdirichlet(100, c(1, 1, 1))
y <- rdirichlet(100, c(1, 1, 1))
codalm_indep_test(y, x)
require(ggtern)
data("WhiteCells", package = 'ggtern')
image <- subset(WhiteCells, Experiment == "ImageAnalysis")
image_mat <- as.matrix(image[,c("G", "L", "M")])
microscopic <- subset(WhiteCells, Experiment == "MicroscopicInspection")
microscopic_mat <- as.matrix(microscopic[,c("G", "L", "M")])
x <- image_mat / rowSums(image_mat)
y <- microscopic_mat / rowSums(microscopic_mat)
codalm_indep_test(y, x)