[R-sig-ME] GWS in MCMCglmm and INLA

[Eder David Borges da Silva]

Thanks Jarrod,
My comparisons between MCMC and INLA worked perfectly
Thank you
Éder

2012/2/11 Jarrod Hadfield <j.hadfield at ed.ac.uk>:
> Hi,
>
> dados$Z<-Z
> rr.mcmc.fit<-MCMCglmm(diametro~1,random=~idv(Z),data=dados,family="gaussian")
>
> would be one way of dong it. Not very efficient for setting up the model,
> but once MCMCing it should be OK.
>
> Cheers,
>
> Jarrod
>
>
> Quoting Eder David Borges da Silva <eder at leg.ufpr.br> on Fri, 10 Feb 2012
> 16:51:49 -0200:
>
>> Dear R user,
>> Just to better understand the GWS (genomic Wide Select), I would like
>> to adjust the model by making the inference in different ways, I could
>> adjust using the INLA, MCMC would like to use, especially with the
>> function MCMCglmm, but I could not understand if this is possible, so
>> I want your help.
>> The code is:
>> ### Seleção Genomica Ampla - Genomic Wide Select
>>
>> #browseURL('http://www.infoteca.cnptia.embrapa.br/bitstream/doc/883425/1/Doc210.pdf')
>> #pg 54
>> ###-----------------------------------------------------###
>> rm(list=ls())
>> require(INLA)
>> require(MCMCglmm)
>> ###-----------------------------------------------------###
>> dados <- data.frame(ind=c(1:5),
>>                    diametro=c(9.87,14.48,8.91,14.64,9.55),
>>                    M1=c(2,1,0,1,1),
>>                    M2=c(0,1,2,0,0),
>>                    M3=c(0,0,0,1,0),
>>                    M4=c(0,0,0,0,1),
>>                    M5=c(2,1,0,1,1),
>>                    M6=c(0,1,0,0,1),
>>                    M7=c(0,0,2,0,0))
>> dados
>> ###-----------------------------------------------------###
>> ### Create Z matrix
>> Z  <- as.matrix(dados[,3:ncol(dados)])
>> ### change effects de 0 1 2 para -1 0 1
>> Z <- apply(Z,2,function(x) ifelse(x==1,-1, ifelse(x!=0,1,0)))
>> Z
>> ###-----------------------------------------------------###
>> ### fit in INLA
>> rr.inla.fit = inla(diametro ~ 1 +
>> f(ind,model="z",Z=Z),data=dados,family="gaussian")
>> summary(rr.inla.fit)
>> rr.inla.fit$summary.random
>> ###-----------------------------------------------------###
>> ### fit MCMCglmm
>> rr.mcmc.fit = MCMCglmm(diametro ~ 1, random = Z
>> ,data=dados,family="gaussian")
>> summary(rr.mcmc.fit)
>>
>> random = ????
>>
>> Thanks
>> Éder David Borges da Silva
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>
>
>
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