You can analyse multiple imputations with the survey and mitools package,
and there is a toy example including ordinal logistic regression at
http://faculty.washington.edu/tlumley/survey/svymi.html
If am I reading their documentation correctly, 'mice' creates what Rubin
calls 'proper imputations', for which the calculations are correct
('improper' imputations are more efficient but the simple variance
calculations are wrong).
The bootstrap approach that Stas Kolenkov pointed out looks attractive as
long as it is computationally feasible.
-thomas
On Mon, 3 Nov 2008, Thomas Soehl wrote:
Hello:
I am working with a stratified survey dataset with sampling weights
and I want to use multiple imputation to help with missingness.
1. Is there a way to run an ordered logistic regression using both a
multiply imputed dataset (i.e. from mice) and adjust for the survey
characteristics using the weight variable? The Zelig package is able
to do binary logistic regressions for survey data and handle the
missing data (logit.survey) but I could not find a way to do both for
an ordered logistic model.
2. I assume I should use the weights in the process of creating the
multiply imputed datasets as well. Is there a way to do so in any of
the multiple imputation packages in R?
Thanks so much
Thomas Soehl
---
Department of Sociology - UCLA
Los Angeles, CA 90095
[EMAIL PROTECTED]
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