[R] Nested variance-covariance matrix in Multilevel model

[Spencer Graves]

	  What's your error message?  I see a syntax error in

random=list(Probe=pdBlocked(list(~1,pdCompSymm(~1
),End=~1,ProbeNo=pdCompSymm(~1))

	  I count 5 open parens and 3 close parens.  I know that might have 
been part of what you copied into this email and not what you gave R. 
However, it sounds like you have several problems.

	  Have you seen Pinheiro and Bates (2000) Mixed-effects models in S and 
S-PLUS (Springer)?  This is the premier (almost mandatory) reference for 
'lme'.  Moreover, the "~\library\nlme\scripts" subdirectory of the R 
installation directory (at least under Windows) contains files "ch01.R", 
"ch02.R", ..., "ch06.R", "ch08.R" containing essentially all the S 
commands used in the book, organized by chapter.  This can save endless 
hassles with spelling problems (and minor syntax differences between 
S-Plus and R).  Sections 1.5 and 1.6, pp. 40-72, discuss analyses if two 
different nested experiments.

	  If just want a standard nested analysis, this should be adequate.  If 
you have other special things happening that I missed in skimming your 
email, please explain why the analyses described in Pinheiro and Bates 
do you apply.  In doing so, please include a simple, self-contained 
example showing what you tried and why you think it's not adequate. 
Please don't send us a megabyte of data.  Instead, please either (a) 
modify an example from the book or some other R documentation  or (b) 
generate phony data with a very few lines of R code.

	  Hope this helps.
	  Spencer Graves

Tobias Guennel wrote:
 > I completely forgot to supply the R code I tried:
 > 
vov1i2<-read.table("VOV1_INHIBITED6-16-2006-13h35min33sec.txt",header=TRUE)
 > 
test.lme<-lme(fixed=log2(Intensity)~End+logpgc,random=list(Probe=pdBlocked(list(~1,pdCompSymm(~1 

 > ),End=~1,ProbeNo=pdCompSymm(~1)),data=vov1i2)
 >
 > It doesn't look right to me and it produces an error too.
 >
 > It should be something around those lines though.
 >
 > Tobias Guennel

Tobias Guennel wrote:
> Dear R community,
> 
> I have trouble implementing a nested variance-covariance matrix in the 
> lme function.
> 
> The model has two fixed effects called End and logpgc, the response 
> variable is the logarithm to base 2 of  Intensity ( log2(Intensity) ) 
> and the random effects are called Probe and ProbeNo.
> The model has the following nesting structure: A Pixel is nested within 
> the ProbeNo,the ProbeNo is within the ProbeEnd ( there are two ends for 
> every probe), and the ProbeEnd is within the Probe.
> 
> Now the problem I have is that the variance-covariance structure of the 
> model is quite complex and I can not find the right syntax for fitting 
> it in the lme function.
> 
> The variance-covariance structure  is a block diagonal matrix of the form,
>           V1   0       0
>  V=    0      V2    0
>           0      0        V3
> 
> where V1...V3 are of the structure:
>           v11      v12
>   V1=                          and so on.
>           v21      v22
> 
> V1...V3 are assumed to have a compound symmetric variance-covariance 
> structure and therefore the submatrices are of the form:
>                    Lambda   Delta1       Delta1 ...   Delta1
>                    Delta1      Lambda    Delta1 ...   Delta1
> v11=v22=    .......
> 
>                    Delta1   .....                               Lambda
>                   
>                    Delta2      Delta2       Delta2 ...   Delta2
>                    Delta2      Delta2       Delta2 ...   Delta2
> v12=v21=    .......
> 
>                    Delta2   .....                               Delta2
> 
> The elements of these submatrices depend only upon the three covariance 
> parameters: the compound symmetry parameter delta; the variance of 
> random effect sigma^2g; and the residual variance sigma^2. I have 
> formulas for the submatrices Lambda,Delta1 and Delta2 which I can't 
> really paste in here.
> 
> The SAS code dealing with this model is the following:
> 
> proc mixed data=rnadeg.pnau;
> title 'CV structure for PNAU';
> class probepos probeno end probe pixelid newprobeid;
> model logPM=end logpgc / ddfm=satterth;
> random probeno newprobeid / subject=probe type=cs;
> lsmeans end / diff cl; run;
> 
> Any ideas are appreciated a lot since I am kind of stuck at this point.
> 
> Thank you
> Tobias Guennel
> 
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