mice.mids {mice} | R Documentation |
Takes a mids
object, and produces a new object of class mids
.
## S3 method for class 'mids' mice(obj, maxit=1, diagnostics=TRUE, printFlag=TRUE, ...)
obj |
An object of class |
maxit |
The number of additional Gibbs sampling iterations. |
diagnostics |
A Boolean flag. If |
printFlag |
A Boolean flag. If |
... |
Named arguments that are passed down to the elementary 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.
http://www.jstatsoft.org/v45/i03/
imp1 <- mice(nhanes,maxit=1) imp2 <- mice.mids(imp1) # yields the same result as imp <- mice(nhanes,maxit=2) # for example: # # > imp$imp$bmi[1,] # 1 2 3 4 5 # 1 30.1 35.3 33.2 35.3 27.5 # > imp2$imp$bmi[1,] # 1 2 3 4 5 # 1 30.1 35.3 33.2 35.3 27.5 #