inst/doc/codalm_quickstart.R

In codalm: Transformation-Free Linear Regression for Compositional Outcomes and Predictors

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup--------------------------------------------------------------------
library(codalm)
require('future')
require('future.apply')

## ----load_data----------------------------------------------------------------
data("educFM")
father <- as.matrix(educFM[,2:4])
father <- father / rowSums(father)
mother <- as.matrix(educFM[,5:7] )
mother <- mother/rowSums(mother)

## ----estimate_B---------------------------------------------------------------
B_est <- codalm(y = father, x = mother)
B_est

## ----bootstrap----------------------------------------------------------------
B_ci <- codalm_ci(y = father, x = mother, nboot = 50,
                   conf = .95)
B_ci$ci_L
B_ci$ci_U

## ----bootstrap_parallel-------------------------------------------------------
ncores <- 2
B_ci_parallel <- codalm_ci(y = father, x = mother, nboot = 50,
                   conf = .95, parallel = TRUE, ncpus = ncores, strategy = 'multisession')
identical(B_ci$ci_L, B_ci_parallel$ci_L)
identical(B_ci$ci_U, B_ci_parallel$ci_U)

## ----independence_test--------------------------------------------------------
set.seed(123)
x <- gtools::rdirichlet(100, rep(1, 3))
y <- gtools::rdirichlet(100, rep(1, 3))
indep_test_pval <- codalm_indep_test(y = y, x = x,
                                     nperms = 100, init.seed = 123)
indep_test_pval

## ----independence_test_parallel-----------------------------------------------
indep_test_pval_parallel <- codalm_indep_test(y = y, x = x,
                                              nperms = 100, init.seed = 123,
                                              parallel = TRUE, ncpus = ncores,
                                              strategy = 'multisession')
indep_test_pval_parallel