brmcoda: Fit Bayesian generalised (non-)linear multilevel...

In florale/multilevelcoda: Estimate Bayesian Multilevel Models for Compositional Data

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

Description

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

if(requireNamespace("cmdstanr")){
  cilr <- complr(data = mcompd, sbp = sbp,
                 parts = c("TST", "WAKE", "MVPA", "LPA", "SB"), idvar = "ID")
  
  # inspects ILRs before passing to brmcoda
  names(cilr$between_logratio)
  names(cilr$within_logratio)
  names(cilr$logratio)
  
  # model with compositional predictor at between and within-person levels
  m1 <- brmcoda(complr = cilr,
                formula = Stress ~ bilr1 + bilr2 + bilr3 + bilr4 +
                                   wilr1 + wilr2 + wilr3 + wilr4 + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  
  # model with compositional outcome
  m2 <- brmcoda(complr = cilr,
                formula = mvbind(ilr1, ilr2, ilr3, ilr4) ~ Stress + Female + (1 | ID),
                chain = 1, iter = 500,
                backend = "cmdstanr")
  }

florale/multilevelcoda documentation built on Sept. 26, 2024, 11:35 p.m.