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

Malcolm Fairbrother M.Fairbrother at bristol.ac.uk
Fri Feb 27 13:47:24 CET 2015

Hi Bonnie,

I have not seen a formal treatment of this issue, but from the Amelia
documentation, my understanding is that if you want an estimate of the
random effects variance, you can just take the average of the estimates
from the model fitted to each imputed dataset. This is true for any
parameter, from the sounds of what Honaker, King, and Blackwell have

 "you can combine directly and use as the multiple imputation estimate of
this parameter, q ̄, the average of them separate estimates"

Even if Zelig doesn't report the RE variance estimates automatically, they
must be "in there" somewhere... I'm sure you can extract them. Or maybe
skip Zelig, and just use Amelia, and extract the estimated RE variances
from each fitted model (presumably using lme4)?


Date: Thu, 26 Feb 2015 21:20:33 -0800
> From: Bonnie Dixon <bmdixon at ucdavis.edu>
> To: Mitchell Maltenfort <mmalten at gmail.com>
> Cc: "r-sig-mixed-models at r-project.org"
>         <r-sig-mixed-models at r-project.org>
> Subject: Re: [R-sig-ME] Best way to handle missing data?
> I actually did try mice also (method "2l.norm"), but it seemed that Amelia
> was preferable for imputation.  Mice seems to only be able to impute one
> variable, whereas Amelia can impute as many variables as have missing data
> producing 100% complete data sets as output.
> However, most of the missing data in the data set I am working with is in
> just one variable, so I could consider using mice, and just imputing the
> variable that has the most missing data, while omitting observations that
> have missing data in any of the other variables.  But the pooled results
> from mice only seem to include the fixed effects of the model, so this
> still leaves me wondering how to report the random effects, which are very
> important to my research question.
> When using Amelia to impute, the packages Zelig and ZeligMultilevel can be
> used to combine the results from each of the models.  But again, only the
> fixed effects seem to be included in the output, so I am not sure how to
> report on the random effects.
> Bonnie
> On Thu, Feb 26, 2015 at 8:33 PM, Mitchell Maltenfort <mmalten at gmail.com>
> wrote:
> > 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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