[R-sig-ME] Missing values in lmer vs. HLM

[Douglas Bates]

I think we would need to know more about the structure of the data and the
models that you wish to fit to it before we could be of any assistance.

To be honest, your question doesn't make sense in the context of lmer.  The
data for lmer must be in the "long form".  That is, each observation
corresponds to a row in the data frame.  If one subject has 5 observations
there will be 5 rows for that subject.  If another has only two
observations there will be two rows.  To me you are describing unbalanced
data, not missing data.  In most cases it is more confusing than
illuminating to think of data in the "wide form", with one row for each
subject and multiple columns for the observations, when working with R.

There is no difficulty with working with unbalanced data in lmer.

On Sat, Jul 4, 2015 at 10:42 AM Carpenter, Tom <tcarpenter at spu.edu> wrote:

> All,
>
> I have a paper in which we are using a within-person model using
> multi-level modeling. I ran the models in lmer in R, although we had a
> substantial portion of people for whom at least one observation is still
> missing. My understanding is that the default is to drop that person
> entirely (e.g., na.action=na.omit) ….is that correct? My understanding was
> that the HLM software (e.g., by SSI) and most other multi-level modeling
> programs can still run the models based on the remaining observations
> (e.g., you may have 4 out of 5 observations per person and still be able to
> run the model).
>
> I would love to know if it is possible to do that in lmer or if some
> solution is present. For example, is it possible to use FIML in lmer?
> Advice for handling this situation would be appreciated, as I’m new to lmer!
>
> Best,
>
>
> Tom Carpenter, Ph.D.
> Assistant Professor, Psychology
> Seattle Pacific University
> 3307 3rd Ave W. Suite 107,
> Seattle, WA, 98119
> tcarpenter at spu.edu<mailto:tcarpenter at spu.edu>
> Office: (206) 281-2916
> Fax: (206) 281-2695
>
>
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