Adjusted K-fold Cross-Validation
An adjustment to K-fold cross-validation is made to reduce bias.
CVDH(X, y, K = 10, REP = 1)
X |
training inputs |
y |
training output |
K |
size of validation sample |
REP |
number of replications |
Algorithm 6.5 (Davison and Hinkley, p.295) is implemented.
Vector of two components comprising the cross-validation MSE and its sd based on the MSE in each validation sample.
A.I. McLeod and C. Xu
Davison, A.C. and Hinkley, D.V. (1997). Bootstrap Methods and their Application. Cambridge University Press.
#Example 1. Variability in 10-fold CV with Davison-Hartigan Algorithm.
#Plot the CVs obtained by using 10-fold CV on the best subset
#model of size 2 for the prostate data. We assume the best model is
#the model with the first two inputs and then we compute the CV's
#using 10-fold CV, 100 times. The result is summarized by a boxplot as well
#as the sd.
NUMSIM<-10
data(zprostate)
train<-(zprostate[zprostate[,10],])[,-10]
X<-train[,1:2]
y<-train[,9]
cvs<-numeric(NUMSIM)
set.seed(123321123)
for (isim in 1:NUMSIM)
cvs[isim]<-CVDH(X,y,K=10,REP=1)[1]
summary(cvs)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.