In this example we use the wheat 599 dataset included in the package. Here we include directly the markers as predictors. We assign a gaussian prior to marker effects (model="BRR"). The within subject covariance matrix (t x t) is modeled as UNstructured, and we assign by default a Scaled-Inverse Chi-square with degree of freedom (scalar) df0, and scale (matrix, t x t) S0. The variance covariance for the residuals is modelled also as UNstructured by default. Other options that can be used for modelling the within subject covariance matrix are "DIAG", "FA" and "REC", likewise for the error.
library(BGLR)
data(wheat)
y<-wheat.Y
X<-wheat.X
X<-scale(X)/sqrt(ncol(X))
ETA<-list(list(X=X,model="BRR"))
fm<-Multitrait(y=y,ETA=ETA,nIter=1000,burnIn=500)
#Residual covariance matrix
fm$resCov
#Genetic covariance matrix
fm$ETA[[1]]$Cov
#Marker effects
fm$ETA[[1]]$beta
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.