codalm_ci {codalm} | R Documentation |
Implements percentile based bootstrapping to estimate the confidence intervals for the regression coefficients when doing linear regression for compositional outcomes and predictors
codalm_ci(
y,
x,
accelerate = TRUE,
nboot = 500,
conf = 0.95,
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 |
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. |
A list, with ci_L
and ci_U
, giving the lower and upper bounds
of each element of the B matrix
data("educFM")
father <- as.matrix(educFM[,2:4])
y <- father / rowSums(father)
mother <- as.matrix(educFM[,5:7] )
x <- mother/rowSums(mother)
codalm_ci(y, x, nboot = 50, conf = .95)