extract_MIdata: Extract a specified number of multiple imputed datasets

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

Extract a specified number of multiple imputed datasets

Description

Extract a specified number of multiple imputed datasets

Usage

extract_MIdata(object, method = c("MAR", "J2R", "CR", "delta"), delta = 0,
  mi.setting = NULL, M = 10, minspace = 2, mess = FALSE)

Arguments

object	object inheriting from class 'remoid'
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".
mi.setting	a list of arguments related to multiple imputation, including trtvar, algorithm, method, include, exclude_chains, thin, start, end, and seed.
M	number of imputed datasets
minspace	minimum number of iterations between iterations to be chosen as imputed values (to prevent strong correlation between imputed datasets in the case of high autocorrelation of the MCMC chains).
mess	logical; should messages be given? Default is TRUE.

Value

A data.frame in which the imputed datasets are stacked onto each other. The variable Imp_ 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.

Examples

# data(schizow)

test = remiod(formula = y6 ~ tx + y0 + y1 + y3, data = schizow,
              trtvar = 'tx', algorithm = 'jags', method="MAR",
              ord_cov_dummy = FALSE, n.adapt = 10, n.chains = 1,
              n.iter = 100, thin = 2, warn = FALSE, seed = 1234)

extdt = extract_MIdata(object=test, method="J2R",mi.setting=NULL, M=10, minspace=2)

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