MCMCglmm covariance matrix question

   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.

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