BME: Bayesian Multi-Environment Model (BME)
In BMTME: Bayesian Multi-Trait Multi-Environment for Genomic Selection Analysis

Description Usage Arguments Value References Examples

Bayesian Multi-Environment Model (BME)

BME(
  Y,
  Z1,
  nIter = 1000L,
  burnIn = 300L,
  thin = 2L,
  bs = ceiling(dim(Z1)[2]/6),
  parallelCores = 1,
  digits = 4,
  progressBar = TRUE,
  testingSet = NULL
)

`Y`	`(matrix)` Phenotypic response where each column is a different environment.
`Z1`	`(matrix)` Matrix design for the genetic effects.
`nIter`	`(integer)` Number of iterations to fit the model.
`burnIn`	`(integer)` Number of items to burn at the beginning of the model.
`thin`	`(integer)` Number of items to thin the model.
`bs`	`(integer)` Number of groups.
`parallelCores`	`(integer)` Number of cores to use.
`digits`	`(integer)` Number of digits of accuracy in the results.
`progressBar`	`(Logical)` Show the progress bar.
`testingSet`	`(object or vector)` Crossvalidation object or vector with the positions to use like testing in a cross-validation test.

If the testingSet is NULL, the function returns the predictions.

Else, if the testingSet is not NULL, the function returns the correlation of the predictions of the cross-validation test.

Montesinos-Lopez, O.A., Montesinos-Lopez, A., Crossa, J., Toledo, F.H., Perez-Hernandez, O., Eskridge, K.M., … Rutkoski, J. (2016). A Genomic Bayesian Multi-trait and Multi-environment Model. G3: Genes|Genomes|Genetics, 6(9), 2725–2744. https://doi.org/10.1534/g3.116.032359.

  data("WheatMadaToy")
  phenoMada <- (phenoMada[order(phenoMada$GID),])

  #Matrix design
  LG <- cholesky(genoMada)
  ZG <- model.matrix(~0 + as.factor(phenoMada$GID))
  Z.G <- ZG %*% LG

  #Pheno data
  Y <- as.matrix(phenoMada[, -c(1)])
  # Check fitting
  fm <- BME(Y = Y, Z1 = Z.G, nIter = 10000, burnIn = 5000, thin = 2, bs = 50)

  # Check predictive capacities of the model with CrossValidation object
  pheno <- data.frame(GID = phenoMada[, 1], Env = '', Response = phenoMada[, 3])
  CrossV <- CV.RandomPart(pheno, NPartitions = 4, PTesting = 0.2, set_seed = 123)

  pm <- BME(Y = Y, Z1 = Z.G, nIter = 10000, burnIn = 5000, thin = 2, bs = 50, testingSet = CrossV)