enhancer (version 1.1.0)

DT_augment: DT_augment design example.

Description

This dataset contains phenotpic data for one trait evaluated in the experimental design known as augmented design. This model allows to obtain BLUPs for genotypes that are unreplicated by dividing the field in blocks and replicating 'check genotypes' in the blocks and unreplicated genotypes randomly within the blocks. The presence of check genotypes (usually cultivars) allows the adjustment of unreplicated genotypes.

Usage

data("DT_augment")

Format

The format is: chr "DT_augment"

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

## AUGMENTED DESIGN EXAMPLE
data(DT_augment)
DT <- DT_augment
head(DT)

# \donttest{

############## sommer ##################
if(requireNamespace("sommer")){
library(sommer)
mix1 <- mmes(TSW ~ Check.Gen,
             random = ~ Block + Genotype:Check,
             data=DT)
summary(mix1)$varcomp

}

############## lme4breeding ##################
if(requireNamespace("lme4breeding")){
library(lme4breeding)
## fit the mixed model and check summary
mix <- lmeb(TSW ~ Check.Gen + (1|Block) + (1|Genotype:Check),
             data=DT)
vc <- VarCorr(mix); print(vc,comp=c("Variance"))
sigma(mix)^2 # error variance
BLUP <- ranef(mix, condVar=TRUE)
condVAR <- lapply(BLUP, function(x){attr(x, which="postVar")}) # take sqrt() for SEs
}

############## end ##################
# }