[R-sig-ME] Best way to handle missing data?

[Mitchell Maltenfort]

Mice might be the package you need

On Thursday, February 26, 2015, Bonnie Dixon <bmdixon at ucdavis.edu> wrote:

> Dear list;
>
> I am using nlme to create a repeated measures (i.e. 2 level) model.  There
> is missing data in several of the predictor variables.  What is the best
> way to handle this situation?  The variable with (by far) the most missing
> data is the best predictor in the model, so I would not want to remove it.
> I am also trying to avoid omitting the observations with missing data,
> because that would require omitting almost 40% of the observations and
> would result in a substantial loss of power.
>
> A member of my dissertation committee who uses SAS, recommended that I use
> full information maximum likelihood estimation (FIML) (described here:
> http://www.statisticalhorizons.com/wp-content/uploads/MissingDataByML.pdf
> ),
> which is the easiest way to handle missing data in SAS.  Is there an
> equivalent procedure in R?
>
> Alternatively, I have tried several approaches to multiple imputation.  For
> example, I used the package, Amelia, which appears to handle the clustered
> structure of the data appropriately, to generate five imputed versions of
> the data set, and then used lapply to run my model on each.  But I am not
> sure how to combine the resulting five models into one final result.  I
> will need a final result that enables me to report, not just the fixed
> effects of the model, but also the random effects variance components and,
> ideally, the distributions across the population of the random intercept
> and slopes, and correlations between them.
>
> Many thanks for any suggestions on how to proceed.
>
> Bonnie
>
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>
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