[R-sig-ME] lme capable of running with missing data?

[Baldwin, Jim -FS]

I think the only way to resolve this is to provide a specific example.

Jim Baldwin
Station Statistician
USDA Forest Service
Albany, California

-----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 7:18 AM
To: Thompson,Paul; r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] lme capable of running with missing data?

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 at 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 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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