missing data in lme, lmer, PROC MIXED
On 28/07/2008, at 11:05 PM, Doran, Harold wrote:
Ken, Does M-Plus actually impute values for the missing cells in the model matrix for the fixed effects? Is this a default behavior of m-plus, or does one need to be cognizant of this and implement a particular imputation strategy?
MPlus has rather poor documentation in this area. Rather than impute I think it assumes multivariate normality of the covariates, or for categorical variables an underlying latent variables. So there is an assumed model for the covariates, this is something that is unavoidable. It is switched on automatically in Mplus. I've tried with some simulated data and it does do something and seems to work properly. With a linear regression on 2 covariates I set half of one covariate to missing. With the missing data option the standard errors are reduced by about 20% compared to complete case which could be quite useful.
In general, this kind of question comes up all the time on the multilevel listserv. There are constant suggestions that many of the multilevel software packages automagically "handle" missing data because they use "maximum likelihood".
A simplification of what actually happens. A useful introductory paper on missing data is http://maven.smith.edu/~nhorton/muchado.pdf and accompanying talk http://maven.smith.edu/~nhorton/muchado-notes.pdf Ken
-----Original Message----- From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Ken Beath Sent: Monday, July 28, 2008 8:22 AM To: MHH Stevens Cc: R Mixed Models; Stevens,Martin Henry H. Dr. Subject: Re: [R-sig-ME] missing data in lme, lmer, PROC MIXED On 28/07/2008, at 9:04 PM, M Henry H Stevens wrote:
Thanks Ken. I have been assuming that they meant missing
covariates (a
subject provided most of the predictors, but not all). So I take it that SAS does no imputation on its own-that the user would
need to do
that (if they wanted?). lme does not do anything like that.
Yes, neither SAS or R or most programs handle missing covariates automatically. The only program I know is MPlus which is a general latent variable modelling program. I turned off the missing data handling as for one model it resulted in an 11 dimensional integration. Ken
Hank On Sat, 2008-07-26 at 22:39 -0400, Ken Beath wrote:
On 26/07/2008, at 7:28 AM, M Henry H Stevens wrote:
Hi folks, I have colleagues who comfortably state that "missing
data" are ok
in "mixed models" - because "the program (PROC MIXED) handles missing data -- I have a hard time imagining what it does. To those of you who use both R and SAS, I was wondering
if you might
share insight into what these do. As far as I know, for lme: 'na.action="na.omit" ' or na.exclude, removes the rows with any missing data.
This depends. If the missing data is the dependent and it
is missing
at random then as mixed models are fitted using maximum
likelihood it
will produce results that are optimal. Roughly (there are
some really
technical definitions for missing data and I haven't
checked them) if
we don't know the outcome and the reason it is missing isn't due to its value or the other data then we can simply leave it out of the likelihood equation it as it has no useful information. A
problem is
when data being missing provides this sort of information
and is very
difficult to model. An example is if observations above a certain value are more likely to be missing. An alternative method of dealing with repeated data is to produce a summary for each subject or cluster, for example by averaging the last three visits. This doesn't correctly handle missing data
although the
loss in efficiency is usually small and it can work well, provided only a small proportion is missing. What R and SAS don't deal with directly is missing data in the covariates. This takes a bit more work, for example using multiple imputation. Here the complete case method where an observation with any missing data is removed will result in a loss of efficiency compared to what can be achieved. Ken
-- Dr. Hank Stevens, Associate Professor 338 Pearson Hall Botany Department Miami University Oxford, OH 45056 Office: (513) 529-4206 Lab: (513) 529-4262 FAX: (513) 529-4243 http://www.cas.muohio.edu/~stevenmh/ http://www.cas.muohio.edu/ecology http://www.muohio.edu/botany/ "If the stars should appear one night in a thousand years,
how would
men believe and adore." -Ralph Waldo Emerson, writer and philosopher (1803-1882)
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