DT_example {lme4breeding}

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"

Source

This data was generated by a potato study.

References

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.

See Also

The core functions of the package lmebreed

Examples

data(DT_example)
DT <- DT_example
A <- A_example
head(DT)

## Compound simmetry (CS) model
ans1 <- lmebreed(Yield~Env + (1|Name) + (1|Env:Name),
             data=DT)
vc <- VarCorr(ans1); print(vc,comp=c("Variance"))

BLUP <- ranef(ans1, condVar=TRUE)$Name
SEs <- attr(BLUP, which="postVar")[,,]



## Compound simmetry (CS) + Diagonal (DIAG) model
## with relationship matrix
Z <- with(DT, smm(Env))
csdiagFormula <- paste0( "Yield ~ Env + (", paste(colnames(Z), collapse = "+"), "|| Name)")
for(i in 1:ncol(Z)){DT[,colnames(Z)[i]] <- Z[,i]}
ansCSDG <- lmebreed(as.formula(csdiagFormula),
                    relmat = list(Name = A ),
                    data=DT)
vc <- VarCorr(ansCSDG); print(vc,comp=c("Variance"))

## Compound simmetry (CS) + Diagonal (DIAG) model
## with diagonal residuals
## with relationship matrix
Z <- with(DT, smm(Env))
DT$units <- as.factor(1:nrow(DT))
csdiagFormula <- paste0( "Yield ~ Env",
                        "+(", paste(colnames(Z), collapse = "+"), "|| Name)",
                        "+(0+ ",paste(colnames(Z), collapse = "+"), "|| units)")
for(i in 1:ncol(Z)){DT[,colnames(Z)[i]] <- Z[,i]}
ansCSDG <- lmebreed(as.formula(csdiagFormula),
                    relmat = list(Name = A ),
                    control = lmerControl(
                      check.nobs.vs.nlev = "ignore",
                      check.nobs.vs.rankZ = "ignore",
                      check.nobs.vs.nRE="ignore"
                    ),
                    data=DT)
vc <- VarCorr(ansCSDG); print(vc,comp=c("Variance"))