lme capable of running with missing data?

 That's interesting. SAS uses the sweep approach (it was in fact 
devised by Goodnight). The method used in construction of various 
types of SS does allow you to estimate when cells are missing. I would 
wonder if Type II SS can be done. Type III (despite the incorrect 
statement that they are
illegitimate) and Type IV would work fine. ****

** **

It's really an issue of the manner in which the design matrix is
contructed.****

** **

*From:* Charles Determan Jr [mailto:deter088 at umn.edu]
*Sent:* Friday, February 03, 2012 8:36 AM
*To:* Thompson,Paul; r-sig-mixed-models at r-project.org
*Subject:* Re: [R-sig-ME] lme capable of running with missing 
data?****

** **

Thank you Paul, I do appreciate your response and especially your time.
The reason I am so persistent is that I know the prior data I posted 
was run in SAS (however I don't have the exact coding although I do 
know it was done with PROC MIXED with an unstructured covariance 
structure and REML estimation method) and it provided all the 
interactions.  As such, I have scoured the web and literature as to 
how this could be done with the missing data (timepoints as a result 
of survival).  Perhaps this simply has not yet been done in R and I am 
stuck for the time being.  None-the-less, I want to be certain before I give up on running this type of analysis in R.

Thanks again,****

On Fri, Feb 3, 2012 at 8:26 AM, Thompson,Paul < 
Paul.Thompson at sanfordhealth.org> wrote:****

Charles:

I did suggest the use of specific contrasts to do the analysis with 
missing cells. I played around, and just have to admit that this is 
not possible. I tried to use standard construction techniques to 
produce main effects using contrast coding, and then multiply those to 
produce interactions. This does not work. It may be possible to use 
orthonormalization and the sweep operator to produce a consistent 
estimator, but I ran out of time to work on this.

What you can do is convert the design to a single factor, and do the 
analysis with specific contrasts, recognizing that this will not 
enable you to get to specific things like interaction effects. To 
understand why, consider the situation with a 2 x 2, where one cell is entirely missing.
You have lost 1 df for the design, and the interaction is entirely missing.
You can estimate and test specific contrasts, but you can't even 
really test the A factor or the B factor. If Cell(2,2) is missing, you 
can test Cell (1,1) v Cell(1,2) and you can test Cell (2,1) v Cell 
(1,1), but neither of these is the test of the "main effect" of A or 
B. When you have larger designs with 2 or 3 factors, the comparisons 
again have fewer df than should be encountered. This means that the 
interactions are not defined properly.

Do you NEED the interactions for theoretical purposes, or are they 
there simply for completedness? Are the cells missing due to your 
design or due to happenstance?

It is the case that fractional factorial designs eliminate cells from 
the design to estimate main effects and losing the ability to estimate 
interactions. So, missing cells, when planned for appropriately, can 
result in appropriate analysis. I am not sure how to run mixed models 
with fractional factorials, however.****


-----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 Charles 
Determan Jr
Sent: Friday, February 03, 2012 7:06 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] lme capable of running with missing data?

Greetings,

Some of you may recognize my name from a few related posts but I just 
have general question that perhaps can be clarified.  I have read 
several times that 'lme' and 'lmer' are techniques capable of running 
data sets with missing values.  Is this true?  I have put up similar 
posts where when I try to run a two or three way interaction mixed 
model I get an error of singularities or X'X not positive.  Does the 
data set need to be formatted in some way where the mixed model can be run with all interactions?
Furthermore, if the missing values are 'not missing at random' is 
there another method to follow for generating the mixed model?  I am 
just confused why I see posts that lme can be run when data is missing.

Regards,****

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