# NOT RUN {
####=========================================####
#### For CRAN time limitations most lines in the
#### examples are silenced with one '#' mark,
#### remove them and run the examples using
#### command + shift + C |OR| control + shift + C
####=========================================####
#
# data(DT_cpdata)
# DT <- DT_cpdata
# GT <- GT_cpdata
# MP <- MP_cpdata
# #### create the variance-covariance matrix
# A <- A.mat(GT) # additive relationship matrix
# #### look at the data and fit the model
# head(DT)
# mix1 <- mmer(Yield~1,
# random=~vs(id,Gu=A)
# + Rowf + Colf,
# rcov=~units,
# data=DT)
# summary(mix1)
#
# ####=========================================####
# #### adding dominance and forcing the other VC's
# ####=========================================####
#
# DT$idd <- DT$id;
# A <- A.mat(GT) # additive relationship matrix
# D <- D.mat(GT) # dominance relationship matrix
# mm <- matrix(3,1,1);mm ## matrix to fix the var comp
# mix2 <- mmer(Yield~1,
# random=~vs(id, Gu=A, Gt=mix1$sigma_scaled$id, Gtc=mm)
# + vs(Rowf,Gt=mix1$sigma_scaled$Rowf, Gtc=mm)
# + vs(Colf,Gt=mix1$sigma_scaled$Colf, Gtc=mm)
# + vs(idd, Gu=D, Gtc=unsm(1)),
# rcov=~vs(units,Gt=mix1$sigma_scaled$units, Gtc=mm),
# data=DT)
# summary(mix2)
#
# ####====================####
# #### multivariate model ####
# #### 2 traits ####
# ####====================####
# #### be patient take some time
# ans.m <- mmer(cbind(Yield,color)~1,
# random=~ vs(id, Gu=A)
# + vs(Rowf,Gtc=diag(2))
# + vs(Colf,Gtc=diag(2)),
# rcov=~ vs(units),
# data=DT)
# cov2cor(ans.m$sigma$`u:id`)
#
# }
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