Nothing
# summary method for cross validation results
summary.cvData <- function(object,...){
obj <- object
ans <- list()
# summary of method
ans$k <- obj$k
ans$Rep <- obj$Rep
ans$est.method <- obj$VC.est.method
ans$sampling <- obj$sampling
if(obj$sampling=="commit") ans$sampling <- "committed"
ans$nr.ranEff <- obj$nr.ranEff
# number of individuals
if(!is.null(obj$n.DS)){
ans$nmin.DS <- min(obj$n.DS)
ans$nmax.DS <- max(obj$n.DS)
}
# size of TS
ans$nmin.TS <- min(obj$n.TS)
ans$nmax.TS <- max(obj$n.TS)
# Results
# Predictive ability
colmean.pa <- colMeans(obj$PredAbi)
ans$se.pa <- format(sd(colmean.pa)/sqrt(length(colmean.pa)),digits=4,nsmall=4)
ans$min.pa <- format(min(obj$PredAbi),digits=4,nsmall=4)
ans$mean.pa <- format(mean(obj$PredAbi),digits=4,nsmall=4)
ans$max.pa <- format(max(obj$PredAbi),digits=4,nsmall=4)
# Spearman's rank correlation
if(!is.null(obj$rankCor)){
colmean.rc <- colMeans(obj$rankCor)
ans$se.rc <- format(sd(colmean.rc)/sqrt(length(colmean.rc)),digits=4,nsmall=4)
ans$min.rc <- format(min(obj$rankCor),digits=4,nsmall=4)
ans$mean.rc <- format(mean(obj$rankCor),digits=4,nsmall=4)
ans$max.rc <- format(max(obj$rankCor),digits=4,nsmall=4)
}
# Bias
colmean.b <- colMeans(obj$bias)
ans$se.b <- format(sd(colmean.b)/sqrt(length(colmean.b)),digits=4,nsmall=4)
ans$min.b<- format(min(obj$bias),digits=4,nsmall=4)
ans$mean.b <- format(mean(obj$bias),digits=4,nsmall=4)
ans$max.b <-format(max(obj$bias),nsmall=4,digits=4)
# 10% best
if(!is.null(obj$m10)){
ans$se.10 <- format(sd(as.vector(obj$m10))/sqrt(length(obj$m10)),digits=4,nsmall=4)
ans$min.10<- format(min(obj$m10),digits=2,nsmall=2)
ans$mean.10 <- format(mean(obj$m10),digits=2,nsmall=2)
ans$max.10 <-format(max(obj$m10),nsmall=2,digits=2)
}
# Mean squared error
if(!is.null(obj$mse)){
colmean.mse <- colMeans(obj$mse)
ans$se.mse <- format(sd(colmean.mse)/sqrt(length(colmean.mse)),digits=4,nsmall=4)
ans$min.mse <- format(min(obj$mse),digits=3,nsmall=3)
ans$mean.mse <- format(mean(obj$mse),digits=3,nsmall=3)
ans$max.mse <- format(max(obj$mse),digits=3,nsmall=3)
}
# Seed
ans$Seed <- obj$Seed
ans$rep.seed <- obj$rep.seed
class(ans) <- "summary.cvData"
ans
}
# print method for summary.pedigree
print.summary.cvData <- function(x,...){
cat("Object of class 'cvData' \n")
cat("\n",x$k,"-fold cross validation with",x$Rep,"replication(s) \n")
cat(" Sampling: ",x$sampling,"\n")
cat(" Variance components: ",x$est.method,"\n")
cat(" Number of random effects:",x$nr.ranEff,"\n")
if(!is.null(x$nmin.DS)) cat(" Number of individuals: ",x$nmin.DS,"--", x$nmax.DS," \n")
cat(" Size of the TS: ",x$nmin.TS,"--", x$nmax.TS," \n")
cat("\nResults: \n")
cat(" Min \t Mean +- pooled SE \t Max \n")
cat(" Predictive ability: ",x$min.pa," \t ",x$mean.pa,"+-",x$se.pa," \t ",x$max.pa,"\n")
if(!is.null(x$mean.rc)) cat(" Rank correlation: ",x$min.rc," \t ",x$mean.rc,"+-",x$se.rc," \t ",x$max.rc,"\n")
if(!is.null(x$mean.mse)) cat(" Mean squared error: ",x$min.mse," \t ",x$mean.mse,"+-",x$se.mse," \t ",x$max.mse,"\n")
cat(" Bias (reg. slope) ",x$min.b," \t ",x$mean.b,"+-",x$se.b," \t ",x$max.b,"\n")
if(!is.null(x$mean.10)) cat(" 10% best predicted: ",x$min.10," \t ",x$mean.10,"+-",x$se.10," \t ",x$max.10,"\n")
cat("\nSeed start: ",x$Seed,"\n")
cat("Seed replications: \n")
print(x$rep.seed)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.