Nothing
summary.MuFicokm <- function(object, CrossValidation = FALSE, ...){
response <- object$response
Nestdesign <- object$Dnest
nlevel <- object$nlevel
n <- dim(as.matrix(ExtractNestDesign(Nestdesign,nlevel)))[1]
if(CrossValidation){
errLOO <- apply(matrix(1:Nestdesign$n),1,function(x) CrossValidationMuFicokmAll(object,x)$CVerrall)
varLOO <- apply(matrix(1:Nestdesign$n),1,function(x) CrossValidationMuFicokmAll(object,x)$CVvarall)
RMSE.LOO <- mean(errLOO^2)
Q2.LOO <- 1 - sum(errLOO^2)/sum((response[[nlevel]]-errLOO-mean(response[[nlevel]]))^2)
Std.RMSE.LOO <- mean(errLOO^2/varLOO)
}
name.cov <- list()
cov.estimate <- list()
sigma2.estimate <- list()
rho.estimate <- list()
beta.estimate <- list()
objecti <- object$cok[[1]]
name.cov[[1]] <- objecti@covariance@name
cov.estimate[[1]] <- covparam2vect(objecti@covariance)
sigma2.estimate[[1]] <- objecti@covariance@sd2
beta.estimate[[1]] <- objecti@trend.coef
for( i in 2:nlevel){
objecti <- object$cok[[i]]
name.cov[[i]] <- objecti@covariance@name
cov.estimate[[i]] <- covparam2vect(objecti@covariance)
sigma2.estimate[[i]] <- objecti@covariance@sd2
mu.estimate <- objecti@trend.coef
db <- dim(objecti@AR.F)[2]
rho.estimate[[i-1]] <- mu.estimate[1:db]
beta.estimate[[i]] <- mu.estimate[(db+1):length(mu.estimate)]
}
## print
cat("Level 1: parameter estimation \n","\n",
"Covariance type:",name.cov[[1]],"\n",
"Variance estimation:",sigma2.estimate[[1]],"\n",
"Trend estimation:",beta.estimate[[1]],"\n",
"Correlation length estimation:",cov.estimate[[1]],"\n","\n",sep=" ")
## print
for(i in 2:nlevel){
cat("Level ",i,": parameter estimation \n","\n",
"Covariance type:",name.cov[[i]],"\n",
"Variance estimation:",sigma2.estimate[[i]],"\n",
"Trend estimation:",beta.estimate[[i]],"\n",
"Adjustment estimation:",rho.estimate[[i-1]],"\n",
"Correlation length estimation:",cov.estimate[[i]],"\n","\n",sep=" ")
}
if(CrossValidation){
cat("Leave One Cross Validation \n","\n",
"RMSE:",RMSE.LOO,"\n",
"Std RMSE:",Std.RMSE.LOO,"\n",
"Q2:",Q2.LOO,"\n","\n",sep="")
}
return(list(
CovNames = name.cov,
Cov.Val = cov.estimate ,
Var.Val = sigma2.estimate ,
Rho.Val = rho.estimate ,
Trend.Val = beta.estimate
))
}
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