Permutation Test for Linear Independence Between Compositional Outcomes and Predictors

Implements the loss function based permutation test as described in Fiksel et al. (2020) for a test of linear independence between compositional outcomes and predictors.

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 plan.
init.seed	The initial seed for the permutations. Default is 123.

Value

The p-value for the independence test

Examples

# \donttest{
require(gtools)
#> Loading required package: gtools
x <- rdirichlet(100, c(1, 1, 1))
y <- rdirichlet(100, c(1, 1, 1))
codalm_indep_test(y, x)
#> [1] 0.276
# }
# \donttest{
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)
#> [1] 0
# }