Description Usage Arguments Examples
Bayesian Multi-Output Regression Stacking (BMORS)
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Y |
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ETA |
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covModel |
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predictor_Sec_complete |
FALSE by default |
nIter |
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burnIn |
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thin |
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progressBar |
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testingSet |
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parallelCores |
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digits |
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | data("WheatToy")
phenoWheatToy <- phenoWheatToy[order(phenoWheatToy$Env, phenoWheatToy$Gid),]
#Matrix Design
LG <- cholesky(genoWheatToy)
ZG <- model.matrix(~0 + as.factor(phenoWheatToy$Gid))
Z.G <- ZG %*% LG
#Linear Predictor
ETA <- list(Gen = list(X = Z.G, model = 'BRR'))
pheno <- phenoWheatToy[, c(1:3)] #Use only the first trait to do a cv
colnames(pheno) <- c('Line', 'Env', 'Response')
CrossValidation <- CV.RandomPart(pheno, NPartitions = 10, PTesting = 0.2, set_seed = 123)
#Pheno
Y <- as.matrix(phenoWheatToy[, c(3,4)])
#Check predictive capacities of the model
pm <- BMORS(Y, ETA = ETA, nIter = 10000, burnIn = 5000, thin = 2,
testingSet = CrossValidation, digits = 4)
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