tang_MI_RB {remiod} | R Documentation |
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 |
treatment |
name of the treatment variable. |
method |
a method for obtaining multiple-imputed dataset. Options include
|
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 |
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.