BMORS_Env: Bayesian Multi-Output regression stacking for specific...
In BMTME: Bayesian Multi-Trait Multi-Environment for Genomic Selection Analysis

Bayesian Multi-Output regression stacking for specific environment estimations

BMORS_Env(
  data = NULL,
  testingEnv = "",
  ETA = NULL,
  covModel = "BRR",
  predictor_Sec_complete = FALSE,
  nIter = 2500,
  burnIn = 500,
  thin = 5,
  progressBar = TRUE,
  digits = 4
)

`data`	`(data.frame)` Phenotypic response where each column is a different trait and the first column are the name of the environment where it was evaluated.
`testingEnv`	`(string)` Name of the Environment to test.
`ETA`	`(matrix)` This is a two-level list used to specify the regression function (or linear predictor).
`covModel`	`(string)` Name of the covariates model to implement (BRR, BayesA, BayesB, BayesC).
`predictor_Sec_complete`	`(Logical)` FALSE by default.
`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.
`progressBar`	`(Logical)` Show the progress bar.
`digits`	`(integer)` Number of digits of accuracy in the results.

data('MaizeToy')
phenoMaizeToy <- phenoMaizeToy[order(phenoMaizeToy$Env, phenoMaizeToy$Line),]

#Matrix design
LG <- cholesky(genoMaizeToy)
ZG <- model.matrix(~0 + as.factor(phenoMaizeToy$Line))
Z.G <- ZG %*% LG
#Linear Predictor
ETA <- list(Gen = list(X = Z.G, model = 'BRR'))

dataset <- phenoMaizeToy[, 2:5] #Must Include in the first column the environments
#Check predictive capacities of the model
pm <- BMORS_Env(dataset, testingEnv = 'EBU', ETA = ETA, covModel = 'BRR', nIter = 10000,
                burnIn = 5000, thin = 2, progressBar = FALSE, digits = 3)