enhancer (version 1.1.0)

DT_halfdiallel: half diallel data for corn hybrids

Description

This dataset contains phenotpic data for 21 corn hybrids, with 2 technical repetitions, coming from a half diallel design and evaluated for sugar content. The column geno indicates the hybrid and male and female origin columns are included as well.

Usage

data("DT_halfdiallel")

Format

The format is: chr "DT_halfdiallel"

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

Giovanny Covarrubias-Pazaran (2024). lme4breeding: enabling genetic evaluation in the age of genomic data. To be submitted to Bioinformatics.

Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48.

Examples

data("DT_halfdiallel")
DT <- DT_halfdiallel
head(DT)
DT$femalef <- as.factor(DT$female)
DT$malef <- as.factor(DT$male)
DT$genof <- as.factor(DT$geno)

# \donttest{

############# sommer #############
if(requireNamespace("sommer")){
library(sommer)
A <- diag(7); colnames(A) <- rownames(A) <- 1:7;A # if you want to provide a covariance matrix
#### model using overlay
modh <- mmes(sugar~1, 
             random=~vsm(ism(overlay(femalef,malef, sparse = FALSE)), Gu=A) 
             + genof,
             data=DT)
summary(modh)$varcomp

# if ussing mmes=TRUE provide Gu with inverses and give more iterations
# Ai <- solve(A + diag(1e-4,ncol(A),ncol(A)))
# Ai <- as(as(as( Ai,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
# attr(Ai, 'inverse')=TRUE 

}

############# lme4breeding #############
if(requireNamespace("lme4breeding")){
library(lme4breeding)
# overlay matrix to be added to the addmat argument
Z <- with(DT, overlay(femalef,malef) )
## model using overlay without relationship matrix
modh <- lmeb(sugar ~ (1|genof) + (1|fema),
                 addmat = list(fema=Z),
             data=DT)
vc <- VarCorr(modh); print(vc,comp=c("Variance"))

## model using overlay with relationship matrix
## relationship matrix to be added to the relmat argument
A <- diag(7); colnames(A) <- rownames(A) <- 1:7;A
modh <- lmeb(sugar ~ (1|genof) + (1|fema),
                 addmat = list(fema=Z),
                 relmat = list(fema=A),
                 data=DT)
vc <- VarCorr(modh); print(vc,comp=c("Variance"))
sigma(modh)^2 # error variance

BLUP <- ranef(modh, condVar=TRUE)
condVAR <- lapply(BLUP, function(x){attr(x, which="postVar")}) # take sqrt() for SEs

}

############# end #############

# }