inst/md/RKHS_UN_UN.md

In gdlc/BGLR-R: Bayesian Generalized Linear Regression

Random effect model with unstructured covariance matrices

Example adapted from MTM package. The first random effect will be unstructured and will be assigned a Scaled-Inverse Chi-square with degree of freedom (scalar) df0, and scale (matrix, t x t) S0. The following example illustrates how to fit a multiple-trait model using the wheat dataset included in the package for 599 wheat lines and 4 traits. In this example the covariance matrix of the random effect and that of model residuals are UNstructured by default.

library(BGLR)
data(wheat)
K<-wheat.A
y<-wheat.Y

ETA<-list(list(K=K,model="RKHS"))
fm<-Multitrait(y=y,ETA=ETA,nIter=1000,burnIn=500)

#Residual covariance matrix
fm$resCov

#Genetic covariance matrix
fm$ETA[[1]]$Cov

#Random effects
fm$ETA[[1]]$u

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gdlc/BGLR-R documentation built on April 23, 2024, 11:01 p.m.