Create multiple imputed datasets based on assigned imputation method.

Internal function, creates multiple imputed datasets based on assigned imputation method returns multiple imputed datasets stacked onto each other (i.e., long format; optionally including the original, incomplete data).

get_MI_RB(object, treatment, method = c("MAR", "J2R", "CR", "delta"),
  delta = 0, exclude_chains = NULL, start = NULL, end = NULL,
  seed = NULL, thin = NULL, subset = FALSE, include = TRUE,
  mess = TRUE, ...)

Arguments

object: an object of class JointAI
treatment: the variable name of treatment. Reference level of treatment should be coded as 0.
method: a method for obtaining multiple-imputed dataset. Options include MAR, J2R, CR, and Delta adjustment.
delta: specific value used for Delta adjustment, applicable only for method="delta".
exclude_chains: optional vector of numbers, indexing MCMC chains to be excluded from the output.
start: first iteration to be used.
end: last iteration to be used.
seed: optional seed value.
thin: thinning to be applied.
subset: subset of parameters (columns of the mcmc object) to be used.
include: should the original, incomplete data be included? Default is TRUE.
mess: logical, should messages be displayed?
...: optional arguments pass from main function.

Value

A data.frame in which the original data (if include = TRUE) and the imputed datasets are stacked onto each other.
The variable Imputation_ indexes the imputation, while .rownr links the rows to the rows of the original data. In cross-sectional datasets the variable .id is added as subject identifier.