enhancer (version 1.1.0)

DT_expdesigns: Data for different experimental designs

Description

The following data is a list containing data frames for different type of experimental designs relevant in plant breeding:

1) Augmented designs (2 examples)

2) Incomplete block designs (1 example)

3) Split plot design (2 examples)

4) Latin square designs (1 example)

5) North Carolina designs I,II and III

How to fit each is shown at the Examples section. This may help you get introduced to experimental designs relevant to plant breeding. Good luck.

Format

Different based on the design.

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_expdesigns)
DT <- DT_expdesigns
names(DT)
data1 <- DT$au1
head(data1)
## response variable: "yield"
## check indicator: "entryc" ('nc' for all unreplicated, but personal.name for checks)
## blocking factor: "block"
## treatments, personal names for replicated and non-replicated: "trt"
## check no check indicator: "new"

# \donttest{

############ sommer ##############
if(requireNamespace("sommer")){
library(sommer)
mix1 <- mmes(yield~entryc,
             random=~block+trt,
             rcov=~units,
             data=data1)
summary(mix1)$varcomp

}

############ lme4breeding ##############
if(requireNamespace("lme4breeding")){
library(lme4breeding)
mix1 <- lmeb(yield~entryc + (1|block)+(1|trt),
             data=data1)
vc <- VarCorr(mix1); print(vc,comp=c("Variance"))
sigma(mix1)^2 # error variance
BLUP <- ranef(mix1, condVar=TRUE)
condVAR <- lapply(BLUP, function(x){attr(x, which="postVar")}) # take sqrt() for SEs

}

# }