Multivariate Imputation by Chained Equations (Iteration Step)
Takes a mids object, and produces a new object of class mids.
mice.mids(obj, newdata = NULL, maxit = 1, printFlag = TRUE, ...)
obj |
An object of class |
newdata |
An optional |
maxit |
The number of additional Gibbs sampling iterations. |
printFlag |
A Boolean flag. If |
... |
Named arguments that are passed down to the univariate imputation functions. |
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.
Stef van Buuren, Karin Groothuis-Oudshoorn, 2000
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/
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) #
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