tang_MI_RB: Implement controlled multiple imputation algorithms proposed...

In xsswang/remiod: Reference-Based Multiple Imputation for Ordinal/Binary Response

Implement controlled multiple imputation algorithms proposed by Tang

Description

Internal function, creates multiple imputed datasets based on assigned imputation method with the algorithm of Tang's sequential modeling.

Usage

tang_MI_RB(object, dtimp, treatment, method = "MAR", delta = 0,
  ord_cov_dummy = FALSE, exclude_chains = NULL, include = FALSE,
  thin = 1)

Arguments

object	object inheriting from class 'remoid'
dtimp	imputed complete data sets from remiod function.
treatment	name of the treatment variable.
method	a method for obtaining multiple-imputed dataset. Options include MAR, J2R, CR, and delta adjustment. Default is MAR.
delta	specific value used for Delta adjustment, applicable only for method="delta".
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.
exclude_chains	optional vector of the index numbers of chains that should be excluded
include	logical, if TRUE, raw data will be included in imputed data sets with imputation ID = 0.
thin	thinning to be applied.

Value

multiple imputed datasets stacked onto each other (i.e., long format; optionally including the original incomplete data).
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.

xsswang/remiod documentation built on Feb. 20, 2023, 9:29 a.m.