GEmetrics_blup {GEmetrics}

Best linear unbiased prediction of genotype-by-environment (GE) metrics

Description

This function calculates the best linear unbiased prediction of the following GE metrics: ecovalence, environmental variance, Finlay and Wilkinson regression and Lin and Binns superiority measure. Ignoring the P matrix resumes to ignoring the condition variance term in the calculation

Usage

GEmetrics_blup(G_hat, metric, P = NULL)

Arguments

G_hat

a matrix of best linear unbiased prediction of environment-specific breeding values for N genotypes (as rows) in J environments (as columns)

metric

a character string indicating what GE metric to consider: "Ecovalence", "EnvironmentalVar", "FinlayWilkRegression", "LinBinns"

P

(optional) a square conditional variance matrix of environment-specific breeding values of dimension NJxNJ, where each row block of size N correspond to an environment and the rows of each block correspond to genotypes

Value

a vector of size N with best linear unbiased prediction of the GE metric

Examples

## Set seed for reproductibility
set.seed(123)

## Load "wheat" dataset from BGLR
data("wheat",package = "BGLR")

## Generate a design data frame for all genotypes in 5 environments
Design <- expand.grid(Genotype=rownames(wheat.A),Environment=paste0("Env",1:5))

## Set sparseness by discarding 80% of the combinations
Design <- Design[-sample(nrow(Design),round(nrow(Design)*4/5)),]

## Simulate phenotypic data with default parameter values
DataSim <- Simulate_MET_data(Design=Design,K=wheat.A)


## Calculate the blup and the conditional variance matrix using simulated variance components
## this step can take several seconds
## note that variance can also be estimated (e.g. using BGLR)
BlupEnvBV <- EnvBV_blup(Pheno=DataSim$Pheno,K=wheat.A,Omega_G=DataSim$Omega_G,
                        Omega_E=DataSim$Omega_E)


## Compute GE metric estimates
GEmetrics_hat <- GEmetrics_blup(G_hat=BlupEnvBV$G_hat,metric="Ecovalence",P=BlupEnvBV$P)
head(GEmetrics_hat)