Last chance! 50% off unlimited learning
Sale ends in
This dataset contains phenotpic data for 41 potato lines evaluated in 3 environments in an RCBD design. The phenotypic trait is tuber quality and we show how to obtain an estimate of DT_example for the trait.
data("DT_example")
The format is: chr "DT_example"
Covarrubias-Pazaran G (2016) Genome assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11(6): doi:10.1371/journal.pone.0156744
The core functions of the package mmer
# NOT RUN {
####=========================================####
#### For CRAN time limitations most lines in the
#### examples are silenced with one '#' mark,
#### remove them and run the examples
####=========================================####
####=========================================####
#### EXAMPLES
#### Different models with sommer
####=========================================####
data(DT_example)
DT <- DT_example
A <- A_example
head(DT)
####=========================================####
#### Univariate homogeneous variance models ####
####=========================================####
## Compound simmetry (CS) model
ans1 <- mmer(Yield~Env,
random= ~ Name + Env:Name,
rcov= ~ units,
data=DT)
summary(ans1)
####===========================================####
#### Univariate heterogeneous variance models ####
####===========================================####
## Compound simmetry (CS) + Diagonal (DIAG) model
ans2 <- mmer(Yield~Env,
random= ~Name + vs(ds(Env),Name),
rcov= ~ vs(ds(Env),units),
data=DT)
summary(ans2)
####===========================================####
#### Univariate unstructured variance models ####
####===========================================####
ans3 <- mmer(Yield~Env,
random=~ vs(us(Env),Name),
rcov=~vs(us(Env),units),
data=DT)
summary(ans3)
# ####==========================================####
# #### Multivariate homogeneous variance models ####
# ####==========================================####
#
# ## Multivariate Compound simmetry (CS) model
# DT$EnvName <- paste(DT$Env,DT$Name)
# ans4 <- mmer(cbind(Yield, Weight) ~ Env,
# random= ~ vs(Name) + vs(EnvName),
# rcov= ~ vs(units),
# data=DT)
# summary(ans4)
#
# ####=============================================####
# #### Multivariate heterogeneous variance models ####
# ####=============================================####
#
# ## Multivariate Compound simmetry (CS) + Diagonal (DIAG) model
# ans5 <- mmer(cbind(Yield, Weight) ~ Env,
# random= ~ vs(Name) + vs(ds(Env),Name),
# rcov= ~ vs(ds(Env),units),
# data=DT)
# summary(ans5)
#
# ####===========================================####
# #### Multivariate unstructured variance models ####
# ####===========================================####
#
# ans6 <- mmer(cbind(Yield, Weight) ~ Env,
# random= ~ vs(us(Env),Name),
# rcov= ~ vs(ds(Env),units),
# data=DT)
# summary(ans6)
# }
Run the code above in your browser using DataLab