Nothing
print.RGE<-function(x,...)
{
sv<-x[grep("sv_",rownames(x)),]
mu<-x[grep("Predicted_",rownames(x)),]
bysi<-mu-1.96*sqrt(sv)
namesbysi<-rownames(bysi)
namesbysi<-gsub("Predicted","BYSI",namesbysi)
rownames(bysi)<-namesbysi
cat("--------------------------------------------\n")
cat("Summary of predicted value of each genotypes\n")
cat("--------------------------------------------\n")
mu<-bayes.posterior(mu,...)
print(mu)
cat("\n")
cat("\n")
cat("-----------------------------------------------------\n")
cat("Summary of stability variance value of each genotypes\n")
cat("-----------------------------------------------------\n")
sv<-bayes.posterior(sv,...)
print(sv)
cat("\n")
cat("\n")
cat("-----------------------------------------------------------\n")
cat("Summary of bayesian yield stability index of each genotypes\n")
cat("-----------------------------------------------------------\n")
bysi<-bayes.posterior(bysi,...)
print(bysi)
}
plot.RGE<-function(x,labelg="Predicted value",
labelsv="Stability variance",
labelby="Bayesian yield stability index",margin=c(1, 0.8, 0, 0.8),...)
{
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar))
resumen<-summary.RGE(x,...)
Y<-resumen[[1]]
par(mai=margin)
tmp<-Y[order(Y[,"BE_mean"],Y[,"lower"]),]
delta<-(max(Y[,"upper"])-min(Y[,"lower"]))/50
nm1<-nm2<-nm<-gsub("Predicted_","",rownames(tmp))
tmp1<-tmp2<-tmp
for (i in seq(1,length(nm),by=2)){nm1[i]<-NA;tmp1[i,]<-NA}
for (i in seq(2,length(nm),by=2)){nm2[i]<-NA;tmp2[i,]<-NA}
limites<-TRUE
if (limites==TRUE){limites<-c(min(tmp[,"lower"],na.rm=TRUE)-delta,max(tmp[,"upper"],na.rm=TRUE)+delta)}
plot(y=1:nrow(tmp1),x=tmp1[,"BE_mean"] , axes = FALSE,xlim=limites,xlab=labelg,ylab="",pch=18)
axis(2, 1:nrow(tmp),nm1,las=2,cex.axis=0.9,tick=FALSE)
axis(1)
arrows ( y0=1:nrow(tmp1) ,x0=tmp1[,"upper"] , y1=1:nrow(tmp1) , x1=tmp1[,"lower"] , angle = 90 , code = 3 , length =0.01)
points(y=1:nrow(tmp2),x=tmp2[,"BE_mean"] , pch=18, col="darkgreen")
axis(4, 1:nrow(tmp2),nm2,las=2,cex.axis=0.9,tick=FALSE,col.axis="darkgreen")
arrows ( y0=1:nrow(tmp2) ,x0=tmp2[,"upper"] , y1=1:nrow(tmp2) ,x1= tmp2[,"lower"] , angle = 90 , code = 3 , length =0.01,col="darkgreen")
Y<-resumen[[2]]
par(mai=margin)
tmp<-Y[order(Y[,"BE_mean"],Y[,"lower"]),]
delta<-(max(Y[,"upper"])-min(Y[,"lower"]))/50
nm1<-nm2<-nm<-gsub("sv_","",rownames(tmp))
tmp1<-tmp2<-tmp
for (i in seq(1,length(nm),by=2)){nm1[i]<-NA;tmp1[i,]<-NA}
for (i in seq(2,length(nm),by=2)){nm2[i]<-NA;tmp2[i,]<-NA}
limites<-TRUE
if (limites==TRUE){limites<-c(min(tmp[,"lower"],na.rm=TRUE)-delta,max(tmp[,"upper"],na.rm=TRUE)+delta)}
plot(y=1:nrow(tmp1),x=tmp1[,"BE_mean"] , axes = FALSE,xlim=limites,xlab=labelsv,ylab="",pch=18)
axis(2, 1:nrow(tmp),nm1,las=2,cex.axis=0.9,tick=FALSE)
axis(1)
arrows ( y0=1:nrow(tmp1) ,x0=tmp1[,"upper"] , y1=1:nrow(tmp1) , x1=tmp1[,"lower"] , angle = 90 , code = 3 , length =0.01)
points(y=1:nrow(tmp2),x=tmp2[,"BE_mean"] , pch=18, col="darkgreen")
axis(4, 1:nrow(tmp2),nm2,las=2,cex.axis=0.9,tick=FALSE,col.axis="darkgreen")
arrows ( y0=1:nrow(tmp2) ,x0=tmp2[,"upper"] , y1=1:nrow(tmp2) ,x1= tmp2[,"lower"] , angle = 90 , code = 3 , length =0.01,col="darkgreen")
Y<-resumen[[3]]
par(mai=margin)
tmp<-Y[order(Y[,"BE_mean"],Y[,"lower"]),]
delta<-(max(Y[,"upper"])-min(Y[,"lower"]))/50
nm1<-nm2<-nm<-gsub("BYSI_","",rownames(tmp))
tmp1<-tmp2<-tmp
for (i in seq(1,length(nm),by=2)){nm1[i]<-NA;tmp1[i,]<-NA}
for (i in seq(2,length(nm),by=2)){nm2[i]<-NA;tmp2[i,]<-NA}
limites<-TRUE
if (limites==TRUE){limites<-c(min(tmp[,"lower"],na.rm=TRUE)-delta,max(tmp[,"upper"],na.rm=TRUE)+delta)}
plot(y=1:nrow(tmp1),x=tmp1[,"BE_mean"] , axes = FALSE,xlim=limites,xlab=labelby,ylab="",pch=18)
axis(2, 1:nrow(tmp),nm1,las=2,cex.axis=0.9,tick=FALSE)
axis(1)
arrows ( y0=1:nrow(tmp1) ,x0=tmp1[,"upper"] , y1=1:nrow(tmp1) , x1=tmp1[,"lower"] , angle = 90 , code = 3 , length =0.01)
points(y=1:nrow(tmp2),x=tmp2[,"BE_mean"] , pch=18, col="darkgreen")
axis(4, 1:nrow(tmp2),nm2,las=2,cex.axis=0.9,tick=FALSE,col.axis="darkgreen")
arrows ( y0=1:nrow(tmp2) ,x0=tmp2[,"upper"] , y1=1:nrow(tmp2) ,x1= tmp2[,"lower"] , angle = 90 , code = 3 , length =0.01,col="darkgreen")
}
summary.RGE<-function(object,...)
{
sv<-object[grep("sv_",rownames(object)),]
mu<-object[grep("Predicted_",rownames(object)),]
bysi<-mu-1.96*sqrt(sv)
namesbysi<-rownames(bysi)
namesbysi<-gsub("Predicted","BYSI",namesbysi)
rownames(bysi)<-namesbysi
mu<-bayes.posterior(mu)
sv<-bayes.posterior(sv)
bysi<-bayes.posterior(bysi)
list(mu=mu,sv=sv,bysi=bysi)
}
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