So, is there a way in which I can alter the design matrix so the mixed
model will work or is this something that can only be done in SAS
currently?  The output from the SAS run did provide Type III fixed effect
test values.

On Fri, Feb 3, 2012 at 9:14 AM, Thompson,Paul <
Paul.Thompson@sanfordhealth.org> wrote:

>  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@umn.edu]
> *Sent:* Friday, February 03, 2012 8:36 AM
> *To:* Thompson,Paul; r-sig-mixed-models@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@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@r-project.org [mailto:
> r-sig-mixed-models-bounces@r-project.org] On Behalf Of Charles Determan Jr
> Sent: Friday, February 03, 2012 7:06 AM
> To: r-sig-mixed-models@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,****
>
>        [[alternative HTML version deleted]]
>
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