Re: [R] ordered logistic regression of survey data with missing variables

From: Thomas Lumley <tlumley_at_u.washington.edu>
Date: Tue, 04 Nov 2008 11:56:35 -0800 (PST)

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
> soehl_at_ucla.edu
>
>
>
>
> [[alternative HTML version deleted]]
>
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Thomas Lumley			Assoc. Professor, Biostatistics
tlumley_at_u.washington.edu	University of Washington, Seattle

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