An adjustment to K-fold cross-validation is made to reduce bias.
Usage
CVDH(X, y, K = 10, REP = 1)
Arguments
X
training inputs
y
training output
K
size of validation sample
REP
number of replications
Details
Algorithm 6.5 (Davison and Hinkley, p.295) is implemented.
Value
Vector of two components comprising the cross-validation MSE and its sd based on the MSE in
each validation sample.
Author(s)
A.I. McLeod and C. Xu
References
Davison, A.C. and Hinkley, D.V. (1997). Bootstrap Methods and their Application. Cambridge University Press.
See Also
bestglm,
CVHTF,
CVd,
LOOCV
Examples
#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)