sommer (version 4.4.3)

DT_example: Broad sense heritability calculation.

Description

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.

Usage

data("DT_example")

Format

The format is: chr "DT_example"

References

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

See Also

The core functions of the package mmes

Examples

####=========================================####
#### 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 <- mmes(Yield~Env,
             random= ~ Name + Env:Name,
             rcov= ~ units,
             data=DT)
summary(ans1)$varcomp

# ####===========================================####
# #### Univariate heterogeneous variance models  ####
# ####===========================================####
# 
# ## Compound simmetry (CS) + Diagonal (DIAG) model
# DT=DT[with(DT, order(Env)), ]
# ans2 <- mmes(Yield~Env,
#              random= ~Name + vsm(dsm(Env),ism(Name)),
#              rcov= ~ vsm(dsm(Env),ism(units)),
#              data=DT)
# summary(ans2)
# 
# ####===========================================####
# #### Multi-trait variance models               ####
# ####===========================================####
# 
# # stack traits
# traits <- c("Yield","Weight")
# DT[,traits] <- apply(DT[,traits],2,scale)
# DTL <- reshape(DT[,c("Name","Env","Block", traits)], 
#                idvar = c("Name","Env","Block"), 
#                varying = traits,
#                v.names = "value", direction = "long",
#                timevar = "trait", times = traits )
# DTL <- DTL[with(DTL, order(trait,Env)), ]
# head(DTL)
# 
# ## model
# ans1 <- mmes(value~ trait,
#              random= ~ vsm(usm(trait), ism(Name)),
#              rcov= ~ vsm(dsm(trait), ism(units)),
#              data=DTL)
# summary(ans1)$varcomp
# cov2cor(ans1$theta$`vsm(usm(trait), ism(Name`)