codalm_indep_test {codalm} | R Documentation |
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
)
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. |
The p-value for the independence test
require(gtools)
x <- rdirichlet(100, c(1, 1, 1))
y <- rdirichlet(100, c(1, 1, 1))
codalm_indep_test(y, x)
data("educFM")
father <- as.matrix(educFM[,2:4])
y <- father / rowSums(father)
mother <- as.matrix(educFM[,5:7] )
x <- mother/rowSums(mother)
codalm_indep_test(y, x)