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#############################################
## Create a genomic prediction Data object
## from the wheat data in the BLR package
##
##
## author : Valentin Wimmer
## date : 2011 - 11 - 30
##
##############################################
data(wheat)
# X = genotypes
# Y = phenotypes
# assign names
rownames(X) <- colnames(A)
colnames(Y) <- paste("Env",1:4,sep="")
rownames(Y) <- colnames(A)
# create a gpData object
gpWheat <- create.gpData(pheno=Y,geno=X)
gpWheat <- codeGeno(gpWheat)
# predictive ability using Bayesian Lasso
# use prior values from Crossa et al. (2010)
priorCrossa <- list(varE=list(df=4,S=1),lambda = list(shape=0.6,rate=1e-4,value=20,type='random'))
# model M-BL for GY-E1
modMBL <- gpMod(gpWheat,trait=1,model="BL",prior=priorCrossa,nIter=15000,burnIn=5000)
# model PM-BL for GY-E1
modPMBL <- gpMod(gpWheat,trait=1,model="BL",prior=priorCrossa,nIter=15000,burnIn=5000,kin=A)
# extract predicted genetic values and plot versus phenotypic values
plot(predict(modPMBL),gpWheat$pheno[,1,])
cv <- crossVal(gpWheat,trait=1,VC.est="BL",prior=priorCrossa,k=10,Rep=1,Seed=123)
summary(cv)
#Object of class 'cvData'
#
# 10 -fold cross validation with 1 replication(s)
# Sampling: random
# Variance components: reestimated with BL
# Number of random effects: 1279
# Number of individuals: 599 -- 599
# Size of the TS: 59 -- 60
#
#Results:
# Min Mean +- pooled SE Max
# Predictive ability: 0.4035 0.5341 +- NA 0.6614
# Rank correlation: 0.3519 0.4765 +- NA 0.6283
# Mean squared error: 0.492 0.727 +- NA 0.901
# Bias: 0.8252 1.0393 +- NA 1.2426
# 10% best predicted: 0.58 0.58 +- NA 0.58
#
#Seed start: 123
#Seed replications:
#[1] 28758
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