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

In mice: Multivariate Imputation by Chained Equations

Multivariate Imputation by Chained Equations (Iteration Step)

Description

Takes a mids object, performs maxit iterations and produces a new object of class "mids".

Usage

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

Arguments

obj	An object of class mids, typically produces by a previous call to mice() or mice.mids()
newdata	An optional data.frame for which multiple imputations are generated according to the model in obj.
maxit	The number of additional Gibbs sampling iterations. The default is 1.
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:

• To add a few extra iteration to an existing solution.

• If RAM memory is exhausted. Returning to prompt/session level may alleviate such problems.

• To customize convergence statistics at specific points, e.g., after every maxit iterations to monitor convergence.

The imputation model itself is specified in the mice() function and cannot be changed in mice.mids(). The state of the random generator is saved with the mids object. This ensures that the imputations are reproducible.

Value

mice.mids returns an object of class "mids".

See Also

complete, mice, set.seed, mids

Examples

imp1 <- mice(nhanes, maxit = 1, seed = 123)
imp2 <- mice.mids(imp1)

# yields the same result as
imp <- mice(nhanes, maxit = 2, seed = 123)

# verification
identical(imp$imp, imp2$imp)
#

mice documentation built on April 12, 2025, 9:14 a.m.