Fit Bayesian generalised (non-)linear multilevel compositional model via full Bayesian inference

Fit a brm model with multilevel ILR coordinates

Usage

brmcoda(complr, formula, ...)

Arguments

complr: A complr object containing data of composition, ILR coordinates, and other variables used in the model.
formula: A object of class formula, brmsformula: A symbolic description of the model to be fitted. Details of the model specification can be found in brmsformula.
...: Further arguments passed to brm.

Value

A brmcoda with two elements

complr: An object of class complr used in the brm model.
model: An object of class brmsfit, which contains the posterior draws along with many other useful information about the model.

Examples

# \donttest{
if(requireNamespace("cmdstanr")){
  x1 <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID")

  # inspect variables before passing to brmcoda
  get_variables(x1)

  ## model with compositional predictor at between and within-person levels
  m1 <- brmcoda(complr = x1,
                formula = Stress ~ bz1_1 + bz2_1 + bz3_1 + bz4_1 +
                                   wz1_1 + wz2_1 + wz3_1 + wz4_1 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")

  ## model with compositional outcome
  m2 <- brmcoda(complr = x1,
                formula = mvbind(z1_1, z2_1, z3_1, z4_1) ~ Stress + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")

  ## model with compositional predictor and outcome
  x2 <- complr(data = mcompd,
                parts = list(c("TST", "WAKE"), c("MVPA", "LPA", "SB")),
                total = list(c(480), c(960)),
                idvar = "ID",
                transform = "ilr")

  m3 <- brmcoda(complr = x2,
                formula = mvbind(z1_2, z2_2) ~ z1_1 + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  }# }
#> Error: CmdStan path has not been set yet. See ?set_cmdstan_path.