I'm dealing with stratified cluster sampling data. I would like to do multiple imputation first and then use weights in R. I looked at the survey-package for weighting and the mi- and mice-package for imputation but I don't know how to combine them.
0
$\begingroup$
$\endgroup$
1
$\begingroup$
$\endgroup$
Let me make a few points.
No solid imputation method exist in the presence of sampling weights - a little more - it will NEVER exist - to construct such imputation you need to estimate a model for the weights - which is silly - because you can use ML without having to estimate such a model or make any model assumptions for the weights.
My own research (unpublished) indicates you can ignore the weights during the imputation - no published example exist to show this otherwise.
You want to at least take into account "stratified" and "cluster" designs for the imputation - that means you should do multiple group two-level imputation. Don't settle for single level imputation that assumes observations are independent across clusters.
Once you have the imputations done then combining the results is trivial - the weights have no affect on the combining rules
This is too hard - instead of imputation - use model estimation that can take into account the missing data
