Run Gibbs sampler for the random moderator placement design

Usage

prepost_gibbs(
  formula,
  data,
  prepost,
  moderator,
  covariates,
  iter = 1000,
  thin = 1,
  burn = 0,
  offset = 0,
  monotonicity = TRUE,
  stable = TRUE,
  saturated = TRUE,
  priors
)

Arguments

formula: A formula with syntax y ~ t, where y is the (unquoted) name of the outcome and t is the (unquoted) name of the treatment.
data: A data.frame containing variables in the formula, moderator, and covariates arguments.
prepost: A one-sided formula with syntax ~ z, where z is the indicator variable for whether the moderator was measured pre- or post-treatment.
moderator: A one-sided formuala with syntax ~ d, where d is the (unquoted) name of the moderator variable for the CATE.
covariates: A one-sided formula with syntax ~ x1 + x2, where the right-hand side variables signify which covariates the Gibbs will use to try and narrow the bounds.
iter: Integer indicating the number of iterations for the Gibbs sampler.
thin: Integer indicating how often the Gibbs sampler should save a draw.
burn: Integer indicating how many iterations should be performed before saving draws in the Gibbs sampler.
offset: A numeric value indicating the center of the prior distribution for the covariate coefficients.
monotonicity: A logical signifying whether the model assumes monotonicity.
stable: A logical signifying whether the model assumes that the the pre vs post indicator does not affect the moderator under the control condition for treatment.
saturated: A logical indicating whether the coefficients on the covariates are allowed to vary by the principal strata.
priors: A list object containing the priors for the Gibbs sampler. Priors include beta.precision, psi.precision, alpha, y.alpha, and y.beta.

Value

A list object containing Gibbs posterior quantities of interest and parameters.

Examples

x <- "alfa,bravo,charlie,delta"