get_MI_RB {remiod}

Create multiple imputed datasets based on assigned imputation method.

Description

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).

Usage

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,
  ord_cov_dummy = 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.

ord_cov_dummy

optional. specify whether ordinal variables should be treated as categorical variables or continuous variables when they are included as covariates in the sequential imputation models. Default is TRUE, dummy variables will be created accordingly.

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.