[R-sig-ME] MCMCglmm covariance matrix question

[Srivats Chari]

Greetings Dr. Bolker,

I apologize for the delayed response.

Your idea would work ideally.

Would you know if it was possible to set the value as 0? For example-
https://stat.ethz.ch/pipermail/r-sig-mixed-models/2015q4/024036.html

Here they use antedependance model and fix the value as 0.

Let me know what you think.

Regards,
Sri.



On Wed, Dec 2, 2020 at 4:07 PM Ben Bolker <bbolker using gmail.com> wrote:

>    That seems quite difficult to set up in a single analysis.
>    Would it work to analyze 8 of the traits with one model, and then
> analyze the other trait (which you want to treat independently) with a
> separate, univariate model?
>
> On 12/2/20 10:56 AM, Srivats Chari wrote:
> > Greetings,
> >
> > I'm trying to run a multivariate MCMCglmm with 9 traits. My traits are
> > measured mostly at the same time except for 1 trait which is measured
> only
> > once or twice per individual. After reading a some literature on this I
> > have a basic idea on how to structure my dataset. But the problem I am
> > facing is that I need my covariance matrix to be different. I want my
> > covariance matrix to exclude the 1 trait so that all trait can COvary
> > together except for a particular one! So there won't be any
> > within-individual COvariation between the particular trait and others.
> >
> > creating a sample dataset-
> >
> > df<- data.frame(ani_id = as.factor(1:10),
> >
> sex=c("male","female","male","female","male","female","male","female","male","female"),age=c("young","adult","young","adult","adult","young","young",
> > "adult","adult","adult"),value=runif(200,min=1,
> > max=5),year=ceiling(runif(200,min=2010, max=2019)), PC1=runif(200,
> min=0.1,
> > max=0.9))
> > df$value[5:9]<- NA
> > df$trait_id<- as.factor(paste("T",rep(1:10, each=20), sep="_"))
> >
> > ## My Prior
> > prior1 <- list(R = list(V =diag(10), nu = 0.002),
> >                        G = list(G1 = list(V = diag(10), nu = 0.002,
> >                                           alpha.mu = rep(0, 10),
> >                                           alpha.V  = diag(10)*25^2)))
> > ## MCMC model
> > mcmc_trial1<-MCMCglmm(scale(value) ~ factor(sex)+
> >               scale(year) + scale(year^2)+
> >               scale(PC1)+ scale(PC1^2)+
> >               factor(age),
> >             random =~ us(trait_id):ani_id,
> >             rcov =~ idh(trait_id):units,
> >             family = c("gaussian"),
> >             prior = prior1,
> >             nitt=10000,
> >             burnin=1000,
> >             thin=10,
> >             verbose = TRUE,
> >             pr=TRUE,
> >             data = df)
> >
> > So when I see a covariance matrix I want the trait T1 to not covary with
> > any other trait.
> >
> > T1 T2 T3 T4 T5 T6 T7 T8 T9 T10
> > T1 0,1 0 0 0 0 0 0 0 0 0
> > T2 0 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
> > T3 0 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
> > T4 0 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
> > T5 0 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
> > T6 0 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
> > T7 0 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
> > T8 0 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
> > T9 0 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
> > T10 0 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
> >
> > Where I am stuck is I do not know how to structure the covariance matrix
> to
> > exclude T1.
> >
> > Any suggestions or help is much appreciated. :)
> >
> > Regards,
> > Srivats.
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models using r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

	[[alternative HTML version deleted]]