Extract multiple imputed datasets from an object of class JointAI

This function returns a dataset containing multiple imputed datasets stacked onto each other (i.e., long format; optionally including the original, incomplete data).
These data can be automatically exported to SPSS (as a .txt file containing the data and a .sps file containing syntax to generate a .sav file). For the export function the foreign package needs to be installed.

get_MIdat(object, m = 10, include = TRUE, start = NULL, minspace = 50,
  seed = NULL, export_to_SPSS = FALSE, resdir = NULL, filename = NULL)

Arguments

object	object inheriting from class 'JointAI'
m	number of imputed datasets
include	should the original, incomplete data be included? Default is TRUE.
start	the first iteration of interest (see window.mcmc)
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).
seed	optional seed value
export_to_SPSS	logical; should the completed data be exported to SPSS?
resdir	optional; directory for results. If unspecified and export_to_SPSS = TRUE the current working directory is used.
filename	optional; file name (without ending). If unspecified and export_to_SPSS = TRUE a name is generated automatically.

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.

Note

In order to be able to extract (multiple) imputed datasets the imputed values must have been monitored, i.e., imps = TRUE had to be specified in the argument monitor_params in *_imp.

See also

plot_imp_distr

Examples

# fit a model and monitor the imputed values with
# monitor_params = c(imps = TRUE)

mod <- lm_imp(y ~ C1 + C2 + M2, data = wideDF,
              monitor_params = c(imps = TRUE), n.iter = 100)

# Example 1: without export to SPSS
MIs <- get_MIdat(mod, m = 3, seed = 123)


if (FALSE) {
# Example 2: with export for SPSS
# (here: to the temporary directory "temp_dir")

temp_dir <- tempdir()
MIs <- get_MIdat(mod, m = 3, seed = 123, resdir = temp_dir,
                 filename = "example_imputation",
                 export_to_SPSS = TRUE)

}