mice.mids: Multivariate Imputation by Chained Equations (Iteration Step)

Description

Takes a mids object, and produces a new object of class mids.

Usage

mice.mids(obj, maxit = 1, printFlag = TRUE, ...)

Arguments

obj

An object of class mids, typically produces by a previous
call to mice() or mice.mids()

maxit

The number of additional Gibbs sampling iterations.

printFlag

A Boolean flag. If TRUE, diagnostic information
during the Gibbs sampling iterations will be written to the command window.
The default is TRUE.

...

Named arguments that are passed down to the univariate imputation
functions.

Details

This function enables the user to split up the computations of the Gibbs
sampler into smaller parts. This is useful for the following reasons:

RAM memory may become easily exhausted if the number of
iterations is large. Returning to prompt/session level may alleviate these
problems.

The user can compute customized convergence statistics at
specific points, e.g. after each iteration, for monitoring convergence. -
For computing a 'few extra iterations'.

Note: The imputation model itself
is specified in the mice() function and cannot be changed with
mice.mids. The state of the random generator is saved with the
mids object.

References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice:
Multivariate Imputation by Chained Equations in R. Journal of
Statistical Software, 45(3), 1-67.
https://www.jstatsoft.org/v45/i03/