[R-sig-ME] Missing values in lmer vs. HLM
bates at stat.wisc.edu
Sat Jul 4 18:18:01 CEST 2015
By the way, most of us don't know the acronym FIML. I have a suspicion
that it is one of the many "maximum likelihood" estimators defined in the
multilevel modeling literature. To a statistician these expressions are
nonsensical. Once you define the probability model there is only one
possible definition of likelihood and hence only one criterion for the
maximum likelihood estimators to optimize. Creating a different criterion
and saying that the optimizers of this criterion are the "<whatever>
maximum likelihood" estimators is false advertising.
Having said all this I will admit that the original sin, the "REML"
criterion, was committed by statisticians. In retrospect I wish that we
had not incorporated that criterion into the nlme and lme4 packages but, at
the time we wrote them, our work would have been dismissed as wrong if our
answers did not agree with SAS PROC MIXED, etc. So we opted for
bug-for-bug compatibility with existing software.
On Sat, Jul 4, 2015 at 11:09 AM Douglas Bates <bates at stat.wisc.edu> wrote:
> 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:
>> 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!
>> 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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>> R-sig-mixed-models at r-project.org mailing list
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