R: Adjusted K-fold Cross-Validation

CVDH {bestglm} | R Documentation

## Adjusted K-fold Cross-Validation

### Description

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)

[Package bestglm version 0.36 Index]