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Simon Blomberg

2003-Jul-22 23:33 UTC

[R] animal models and lme

Hi,

You should look at Pinheiro and Bates (2000) Mixed-effects models in S and
S-Plus. It describes how to format the correlation matrix to pass to functions
lme and gls. Basically, the correlation matrix has to be one of the corStruct
classes, probably corSymm for your example. So in the call to lme (or gls if you
really have no random effects), use something like:
correlation=corSymm(AGRM[lower.tri(AGRM)], fixed=TRUE). Anyway, it's all
there in the book.

HTH,

Simon.

Simon Blomberg, PhD
Depression & Anxiety Consumer Research Unit
Centre for Mental Health Research
Australian National University
http://www.anu.edu.au/cmhr/
Simon.Blomberg at anu.edu.au  +61 (2) 6125 3379

> -----Original Message-----
> From: Jarrod Hadfield [mailto:jarrod.hadfield at imperial.ac.uk]
> Sent: Wednesday, 23 July 2003 5:52 PM
> To: r-help at stat.math.ethz.ch
> Subject: [R] animal models and lme
> 
> 
> 
> -- 
> Hi R users,
> 
> I'm trying to fit an animal model using lme and cannot fit the 
> corelation matrix which describes the additive genetic correlation 
> between individuals.
> 
> I have created a test data set (pedigree) where the first column 
> specifies the id of each individual, and the second column contains 
> simulated trait values. AGRM is the corresponding additive genetic 
> relationship matrix whose elements are equal to the genetic 
> correlation between individuals.
> 
> The test data is simulated to have a heritibility of 0.8 (i.e. 80% of 
> the variation can be explained by genetic effects)
> 
> My initial attempt has been:
> 
> CM<-pdSymm(AGRM, ~1|animal)
> lme(trait~1, data=pedigree, CM)
> 
> but which ever combination I use I always get "invalid 
> formula for groups".
> 
> Does any one know how to code this sort of model?
> 
> Thanks for your help,
> 
> Jarrod Hadfield.
> 
> copy and paste from here to get the test data (Sorry the correlation 
> matrix is so big)
> 
> ##############################################################
> ################################
> 
>
animal<-c("Newbird1","Newbird2","a","b","c","d","e","f","g","h
>
","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w",
>
"x","y","z","aa","bb","cc","dd","ee","ff","gg","hh","ii","jj",
> "kk","ll","mm")
> 
> trait<-c(-1.10988343,-0.95061690,-1.05596407,-0.20075671,-0.98747062,1.72132706,1.33973268,0.30333354,0.34969961>
,0.70681745,-0.15210172,-1.05918719,-0.32665059,0.55423824,-0.> 81324407,-0.61359451,,0.65936390,-0.92295239,0.87285686,1.1150
> 8781,1.30904325,0.83437615,,0.72948628,,0.63043694,-0.40078431
> ,,1.20814060,,0.69233357,-1.51568342,-0.16235203,-2.26109886,1.21242313,0.04010594,0.06988673,-0.70644384,->
0.69870290,-0.02043294,-0.57957532,-0.78726879,0.20574982,0.27> 469486,-0.25899545)
> 
> pedigree<-data.frame(cbind(animal, trait))
> 
> AGRM<-as.matrix(cbind(c(1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
> 0,0,0,0,0,0,0,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0),c(0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0>
,0,0,0,0,0,0,0,0,0.5,0.5,0.5),c(0,0,1,0,0,0,0,0,0,0,0.5,0.5,0.> 5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0.25,0.25,0.25,0,0,0
> ,0,0,0,0,0,0),c(0,0,0,1,0,0,0,0,0,0,0.5,0.5,0.5,0.5,0,0,0,0,0,
> 0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0,0,0,0,0,0,0,0,0),c(0,0,0,0,1,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0.5,0,0,0,0,0,0,>
0,0,0,0,0,0.5,0.5,0.5,0.25,0.25,0.25,0.25,0.25,0.25,0,0,0),c(0
,0,0,0,0,1,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0.5,0,0,0,0>
,0,0,0,0,0,0,0,0,0,0,0.75,0.75,0.75,0.25,0.25,0.25,0,0,0),c(0,
0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0.5,>
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),c(0,0,0,0,0,0,0,1,0,0,0> ,0,0,0,0,0,0,0,0.5,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
> ,0,0,0),c(0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,
> 0.5,0.5,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0.25,0.25,0.25),c(0,0,0,
0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5!>  , 
> 0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25),c(0,0,0.5,0.5,
> 0,0,0,0,0,0,1,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.
> 25,0.25,0.25,0.25,0,0,0,0,0,0,0,0,0),c(0,0,0.5,0.5,0,0,0,0,0,0
> ,0.5,1,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0.2
> 5,0.25,0,0,0,0,0,0,0,0,0),c(0,0,0.5,0.5,0,0,0,0,0,0,0.5,0.5,1,
> 0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0.25,0.25,0,0,
> 0,0,0,0,0,0,0),c(0,0,0.5,0.5,0,0,0,0,0,0,0.5,0.5,0.5,1,0,0,0,0
> ,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0)
> ,c(0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,1,0.5,0.5,0.5,0,0,0,0,0,0,0
> ,0,0,0,0,0.25,0.25,0.25,0.75,0.75,0.75,0.25,0.25,0.25,0,0,0),c
> (0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0.5,1,0.5,0.5,0,0,0,0,0,0,0,0
> ,0,0,0,0.25,0.25,0.25,0.5,0.5,0.5,0.25,0.25,0.25,0,0,0),c(0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0.5,0.5,1,0.5,0,0,0,0,>
0,0,0,0,0,0,0,0.25,0.25,0.25,0.5,0.5,0.5,0.25,0.25,0.25,0,0,0)> ,c(0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0.5,0.5,0.5,1,0,0,0,0,0,0,0
> ,0,0,0,0,0.25,0.25,0.25,0.5,0.5,0.5,0.5,0.5,0.5,0,0,0),c(0,0,0
> ,0,0,0,0.!
>  5 
> ,0.5,0,0,0,0,0,0,0,0,0,0,1,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0
> ,0,0,0,0,0,0,0),c(0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0.5,
> 1,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),c(0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0.5,0.5,1,0.5,0,0,0,0>
,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),c(0,0,0,0,0,0,0.5,0.5,0,0,0,0,> 0,0,0,0,0,0,0.5,0.5,0.5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
> 0),c(0,0,0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,1,0.5,0.5
> ,0.5,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0.25,0.25),c(0,0,0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.5,1,0.>
5,0.5,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0.25,0.25),c(0,0,0
,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5>
,1,0.5,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.5,0.5,0.5),c(0,0,0,0
,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0>
.5,1,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0.25,0.25),c(0.5,0,
0.5,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0,0,0,0,0,0,>
0,0,0,0,0,0,1,0.5,0.5,0.125,0.125,0.125,0,0,0,0,0,0,0,0,0),c(0
.5,0,0.5,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0,0,0,0>
,0,0,0,!>  0 
> ,0,0,0,0,0.5,1,0.5,0.125,0.125,0.125,0,0,0,0,0,0,0,0,0),c(0.5,0,0.5,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0,0,0,0,0,>
0,0,0,0,0,0,0,0.5,0.5,1,0.125,0.125,0.125,0,0,0,0,0,0,0,0,0),c> (0,0,0.25,0.25,0.5,0,0,0,0,0,0.25,0.25,0.25,0.5,0.25,0.25,0.25
> ,0.25,0,0,0,0,0,0,0,0,0.125,0.125,0.125,1,0.5,0.5,0.125,0.125,
> 0.125,0.125,0.125,0.125,0,0,0),c(0,0,0.25,0.25,0.5,0,0,0,0,0,0
> .25,0.25,0.25,0.5,0.25,0.25,0.25,0.25,0,0,0,0,0,0,0,0,0.125,0.
> 125,0.125,0.5,1,0.5,0.125,0.125,0.125,0.125,0.125,0.125,0,0,0)
> ,c(0,0,0.25,0.25,0.5,0,0,0,0,0,0.25,0.25,0.25,0.5,0.25,0.25,0.
> 25,0.25,0,0,0,0,0,0,0,0,0.125,0.125,0.125,0.5,0.5,1,0.125,0.12
> 5,0.125,0.125,0.125,0.125,0,0,0),c(0,0,0,0,0.25,0.75,0,0,0,0,0
> ,0,0,0,0.75,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0.125,0.125,0.12
> 5,1.25,0.75,0.75,0.25,0.25,0.25,0,0,0),c(0,0,0,0,0.25,0.75,0,0
> ,0,0,0,0,0,0,0.75,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0.125,0.12
> 5,0.125,0.75,1.25,0.75,0.25,0.25,0.25,0,0,0),c(0,0,0,0,0.25,0.
> 75,0,0,0,0,0,0,0,0,0.75,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0.12
> 5,0.125,0!
>  . 
> 125,0.75,0.75,1.25,0.25,0.25,0.25,0,0,0),c(0,0,0,0,0.25,0.25,0
> ,0,0.5,0,0,0,0,0,0.25,0.25,0.25,0.5,0,0,0,0,0.25,0.25,0.25,0.2
> 5,0,0,0,0.125,0.125,0.125,0.25,0.25,0.25,1,0.5,0.5,0.125,0.125
> ,0.125),c(0,0,0,0,0.25,0.25,0,0,0.5,0,0,0,0,0,0.25,0.25,0.25,0
> .5,0,0,0,0,0.25,0.25,0.25,0.25,0,0,0,0.125,0.125,0.125,0.25,0.
> 25,0.25,0.5,1,0.5,0.125,0.125,0.125),c(0,0,0,0,0.25,0.25,0,0,0
> .5,0,0,0,0,0,0.25,0.25,0.25,0.5,0,0,0,0,0.25,0.25,0.25,0.25,0,
> 0,0,0.125,0.125,0.125,0.25,0.25,0.25,0.5,0.5,1,0.125,0.125,0.1
> 25),c(0,0.5,0,0,0,0,0,0,0.25,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0.25
> ,0.25,0.5,0.25,0,0,0,0,0,0,0,0,0,0.125,0.125,0.125,1,0.5,0.5),
> c(0,0.5,0,0,0,0,0,0,0.25,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.2
> 5,0.5,0.25,0,0,0,0,0,0,0,0,0,0.125,0.125,0.125,0.5,1,0.5),c(0,0.5,0,0,0,0,0,0,0.25,0.25,0,0,0,0,0,0,0,0,0,0,0,0,>
0.25,0.25,0.5,0.25,0,0,0,0,0,0,0,0,0,0.125,0.125,0.125,0.5,0.5,1)))> colnames(AGRM)<-animal
> rownames(AGRM)<-animal
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://www.stat.math.ethz.ch/mailman/listinfo/r-help
>

Jarrod Hadfield

2003-Jul-23 07:52 UTC

head link

[R] animal models and lme

-- 
Hi R users,

I'm trying to fit an animal model using lme and cannot fit the 
corelation matrix which describes the additive genetic correlation 
between individuals.

I have created a test data set (pedigree) where the first column 
specifies the id of each individual, and the second column contains 
simulated trait values. AGRM is the corresponding additive genetic 
relationship matrix whose elements are equal to the genetic 
correlation between individuals.

The test data is simulated to have a heritibility of 0.8 (i.e. 80% of 
the variation can be explained by genetic effects)

My initial attempt has been:

CM<-pdSymm(AGRM, ~1|animal)
lme(trait~1, data=pedigree, CM)

but which ever combination I use I always get "invalid formula for
groups".

Does any one know how to code this sort of model?

Thanks for your help,

Jarrod Hadfield.

copy and paste from here to get the test data (Sorry the correlation 
matrix is so big)

##############################################################################################

animal<-c("Newbird1","Newbird2","a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z","aa","bb","cc","dd","ee","ff","gg","hh","ii","jj","kk","ll","mm")

trait<-c(-1.10988343,-0.95061690,-1.05596407,-0.20075671,-0.98747062,1.72132706,1.33973268,0.30333354,0.34969961,0.70681745,-0.15210172,-1.05918719,-0.32665059,0.55423824,-0.81324407,-0.61359451,,0.65936390,-0.92295239,0.87285686,1.11508781,1.30904325,0.83437615,,0.72948628,,0.63043694,-0.40078431,,1.20814060,,0.69233357,-1.51568342,-0.16235203,-2.26109886,1.21242313,0.04010594,0.06988673,-0.70644384,-0.69870290,-0.02043294,-0.57957532,-0.78726879,0.20574982,0.27469486,-0.25899545)

pedigree<-data.frame(cbind(animal, trait))

AGRM<-as.matrix(cbind(c(1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0),c(0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5),c(0,0,1,0,0,0,0,0,0,0,0.5,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0.25,0.25,0.25,0,0,0,0,0,0,0,0,0),c(0,0,0,1,0,0,0,0,0,0,0.5,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0,0,0,0,0,0,0,0,0),c(0,0,0,0,1,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0.25,0.25,0.25,0.25,0.25,0.25,0,0,0),c(0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.75,0.75,0.75,0.25,0.25,0.25,0,0,0),c(0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),c(0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),c(0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,0.25,0.25,0.25),c(0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,
0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25),c(0,0,0.5,0.5,0,0,0,0,0,0,1,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0.25,0.25,0,0,0,0,0,0,0,0,0),c(0,0,0.5,0.5,0,0,0,0,0,0,0.5,1,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0.25,0.25,0,0,0,0,0,0,0,0,0),c(0,0,0.5,0.5,0,0,0,0,0,0,0.5,0.5,1,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0.25,0.25,0,0,0,0,0,0,0,0,0),c(0,0,0.5,0.5,0,0,0,0,0,0,0.5,0.5,0.5,1,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0),c(0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,1,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.75,0.75,0.75,0.25,0.25,0.25,0,0,0),c(0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0.5,1,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.5,0.5,0.5,0.25,0.25,0.25,0,0,0),c(0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0.5,0.5,1,0.5,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.5,0.5,0.5,0.25,0.25,0.25,0,0,0),c(0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0.5,0.5,0.5,1,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.5,0.5,0.5,0.5,0.5,0.5,0,0,0),c(0,0,0,0,0,0,0.5
,0.5,0,0,0,0,0,0,0,0,0,0,1,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),c(0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0.5,1,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),c(0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0.5,0.5,1,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),c(0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),c(0,0,0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,1,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0.25,0.25),c(0,0,0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.5,1,0.5,0.5,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0.25,0.25),c(0,0,0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,1,0.5,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.5,0.5,0.5),c(0,0,0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0.5,1,0,0,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0.25,0.25),c(0.5,0,0.5,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0,0,0,0,0,0,0,0,0,0,0,0,1,0.5,0.5,0.125,0.125,0.125,0,0,0,0,0,0,0,0,0),c(0.5,0,0.5,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0,0,0,0,0,0,0,0
,0,0,0,0,0.5,1,0.5,0.125,0.125,0.125,0,0,0,0,0,0,0,0,0),c(0.5,0,0.5,0,0,0,0,0,0,0,0.25,0.25,0.25,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,1,0.125,0.125,0.125,0,0,0,0,0,0,0,0,0),c(0,0,0.25,0.25,0.5,0,0,0,0,0,0.25,0.25,0.25,0.5,0.25,0.25,0.25,0.25,0,0,0,0,0,0,0,0,0.125,0.125,0.125,1,0.5,0.5,0.125,0.125,0.125,0.125,0.125,0.125,0,0,0),c(0,0,0.25,0.25,0.5,0,0,0,0,0,0.25,0.25,0.25,0.5,0.25,0.25,0.25,0.25,0,0,0,0,0,0,0,0,0.125,0.125,0.125,0.5,1,0.5,0.125,0.125,0.125,0.125,0.125,0.125,0,0,0),c(0,0,0.25,0.25,0.5,0,0,0,0,0,0.25,0.25,0.25,0.5,0.25,0.25,0.25,0.25,0,0,0,0,0,0,0,0,0.125,0.125,0.125,0.5,0.5,1,0.125,0.125,0.125,0.125,0.125,0.125,0,0,0),c(0,0,0,0,0.25,0.75,0,0,0,0,0,0,0,0,0.75,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0.125,0.125,0.125,1.25,0.75,0.75,0.25,0.25,0.25,0,0,0),c(0,0,0,0,0.25,0.75,0,0,0,0,0,0,0,0,0.75,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0.125,0.125,0.125,0.75,1.25,0.75,0.25,0.25,0.25,0,0,0),c(0,0,0,0,0.25,0.75,0,0,0,0,0,0,0,0,0.75,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0.125,0.125,0.
125,0.75,0.75,1.25,0.25,0.25,0.25,0,0,0),c(0,0,0,0,0.25,0.25,0,0,0.5,0,0,0,0,0,0.25,0.25,0.25,0.5,0,0,0,0,0.25,0.25,0.25,0.25,0,0,0,0.125,0.125,0.125,0.25,0.25,0.25,1,0.5,0.5,0.125,0.125,0.125),c(0,0,0,0,0.25,0.25,0,0,0.5,0,0,0,0,0,0.25,0.25,0.25,0.5,0,0,0,0,0.25,0.25,0.25,0.25,0,0,0,0.125,0.125,0.125,0.25,0.25,0.25,0.5,1,0.5,0.125,0.125,0.125),c(0,0,0,0,0.25,0.25,0,0,0.5,0,0,0,0,0,0.25,0.25,0.25,0.5,0,0,0,0,0.25,0.25,0.25,0.25,0,0,0,0.125,0.125,0.125,0.25,0.25,0.25,0.5,0.5,1,0.125,0.125,0.125),c(0,0.5,0,0,0,0,0,0,0.25,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.5,0.25,0,0,0,0,0,0,0,0,0,0.125,0.125,0.125,1,0.5,0.5),c(0,0.5,0,0,0,0,0,0,0.25,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.5,0.25,0,0,0,0,0,0,0,0,0,0.125,0.125,0.125,0.5,1,0.5),c(0,0.5,0,0,0,0,0,0,0.25,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0.25,0.5,0.25,0,0,0,0,0,0,0,0,0,0.125,0.125,0.125,0.5,0.5,1)))
colnames(AGRM)<-animal
rownames(AGRM)<-animal

iwhite@staffmail.ed.ac.uk

2003-Jul-23 09:58 UTC

head link

[R] animal models and lme

Not convinced that responses so far have addressed the problem. The
model is

y = mu + U + e

where e is a vector of independendent errors with variance ve, and U
is a vector of random effects with covariance matrix va*A, where A is a
known matrix (which we can assume is a correlation matrix). If we know the
ratio (va/ve), this reduces to a GLS problem, but not otherwise. Usually
we have to estimate both ve and va.

=====================================I.White
ICAPB, University of Edinburgh
Ashworth Laboratories, West Mains Road
Edinburgh EH9 3JT
Fax: 0131 650 6564  Tel: 0131 650 5490
E-mail: iwhite at staffmail.ed.ac.uk

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