Multivariate Imputation by Chained Equations (Iteration Step)

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.

Value

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

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.

See also

complete, mice, set.seed, mids

Examples

imp1 <- mice(nhanes, maxit = 1, seed = 123)
#> 
#>  iter imp variable
#>   1   1  bmi  hyp  chl
#>   1   2  bmi  hyp  chl
#>   1   3  bmi  hyp  chl
#>   1   4  bmi  hyp  chl
#>   1   5  bmi  hyp  chl
imp2 <- mice.mids(imp1)
#> 
#>  iter imp variable
#>   2   1  bmi  hyp  chl
#>   2   2  bmi  hyp  chl
#>   2   3  bmi  hyp  chl
#>   2   4  bmi  hyp  chl
#>   2   5  bmi  hyp  chl

# yields the same result as
imp <- mice(nhanes, maxit = 2, seed = 123)
#> 
#>  iter imp variable
#>   1   1  bmi  hyp  chl
#>   1   2  bmi  hyp  chl
#>   1   3  bmi  hyp  chl
#>   1   4  bmi  hyp  chl
#>   1   5  bmi  hyp  chl
#>   2   1  bmi  hyp  chl
#>   2   2  bmi  hyp  chl
#>   2   3  bmi  hyp  chl
#>   2   4  bmi  hyp  chl
#>   2   5  bmi  hyp  chl

# verification
identical(imp$imp, imp2$imp)
#> [1] TRUE
#