sommer (version 2.1)

h2: Broad sense heritability calculation.

Description

This dataset contains phenotpic data for 41 potato lines evaluated in 5 locations across 3 years in an RCBD design. The phenotypic trait is tuber quality and we show how to obtain an estimate of h2 for the trait.

Usage

data("h2")

Format

The format is: chr "h2"

Source

This data was generated by a potato study.

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 function of the package mmer

Examples

####=========================================####
#### For CRAN time limitations most lines in the 
#### examples are silenced with one '#' mark, 
#### remove them and run the examples
####=========================================####
data(h2)
head(h2)
####=========================================####
#### fit the mixed model and extract var.comp
####=========================================####
#ans1 <- mmer2(y~1, random=~Name + Env + Name:Env + Block,data=h2, method="NR")
#vc <- ans1$var.comp
#V_E <- vc[2,1];V_GE <- vc[3,1];V_G <- vc[1,1];Ve <- vc[5,1]
####=========================================####
#### calculate heritability
####=========================================####
#n.env <- length(levels(h2$Env))
#h2c <- V_G/(V_G + V_GE/n.env + Ve/(2*n.env)) #the 2 is a reference for block#
#h2c

####=========================================####
####=========================================####
#### using the 'mmer' function would be fitted as
####=========================================####
####=========================================####
#data(h2)
#Y <- h2$y
#Z1 <- model.matrix(~Name-1,data=h2)
#Z2 <- model.matrix(~Env-1,data=h2)
#Z3 <- model.matrix(~Name:Env-1,data=h2)
#Z4 <- model.matrix(~Block-1,data=h2)
#ETA <- list(Name=list(Z=Z1),
#            Env=list(Z=Z2),
#            Name.Env=list(Z=Z3),
#            Block=list(Z=Z4))

#ans1 <- mmer(Y=Y,Z=ETA)
#vc <- ans1$var.comp
#V_E <- vc[2,1];V_GE <- vc[3,1];V_G <- vc[1,1];Ve <- vc[5,1]
#n.env <- length(levels(h2$Env))
#h2c <- V_G/(V_G + V_GE/n.env + Ve/(2*n.env)) #the 2 is a reference for block#
#h2c