I am not a professional and sorry if I bring any incorrect concept.

When you say impute, I guess you want to replace the missing part
by its conditional expection.

This is not safe if you do non-linear opearations later. An simple example
is the expection of quadratic form, E(X'AX) != E(X)'AE(X). In other words,
for example, if you want to estimate the covariance, better avoiding using
the imputed values directly since they will not give good results, but if
you want to estimate the mean, then it is maybe OK (maybe, I do not
remember my results). Please correct if I am wrong.

I am also interested in those packages. But if you are stuck with finding a
proper package, you may write your own. The most clear explaniation I have
read is wiki.

In order to use EM, you need to specify the distribution of the data, not
sure if it is proper, depending what is your next step. And also, Bayesian
methods and packages I think exist in R to impute the data.

Best wishes,
Jie
On Mon, Jul 23, 2012 at 11:39 AM, David L Carlson <dcarlson@tamu.edu> wrote:

> This is chopped off. What happens next is important. EM can be used to
> compute covariance matrices and conducts  the statistical analysis without
> imputing any missing values or it can be used to impute missing values to
> create one or multiple data sets.
>
> You might find Missing Data by Paul D. Allison to be useful.
>
> ----------------------------------------------
> David L Carlson
> Associate Professor of Anthropology
> Texas A&M University
> College Station, TX 77843-4352
>
>
> > -----Original Message-----
> > From: r-help-bounces@r-project.org [mailto:r-help-bounces@r-
> > project.org] On Behalf Of ya
> > Sent: Sunday, July 22, 2012 5:11 AM
> > To: r-help
> > Subject: Re: [R] EM for missing data
> >
> > hi Greg, David, and Tal,
> >
> > Thank you very much for the information.
> >
> > I found this in SPSS 17.0 missing value manual:
> >
> > EM Method
> >
> > This method assumes a distribution for the partially missing data and
> > bases inferences
> > on the likelihood under that distribution. Each iteration consists of
> > an E step and an
> > M step. The E step finds the conditional expectation of the b
>
> ______________________________________________
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.
>

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