R: K-fold Cross-Validation

CVHTF {bestglm} | R Documentation

## K-fold Cross-Validation

### Description

K-fold cross-validation.

### Usage

    CVHTF(X, y, K = 10, REP = 1, family = gaussian, ...)

### Arguments

`X` | training inputs
`y` | training output
`K` | size of validation sample
`REP` | number of replications
`family` | glm family
`...` | optional arguments passed to `glm` or `lm`

### Details

HTF (2009) describe K-fold cross-validation. The observations are partitioned into K non-overlapping subsets of approximately equal size. Each subset is used as the validation sample while the remaining K-1 subsets are used as training data. When K=n, where n is the number of observations the algorithm is equivalent to leave-one-out CV. Normally K=10 or K=5 are used. When K<n-1, their are may be many possible partitions and so the results of K-fold CV may vary somewhat depending on the partitions used. In our implementation, random partitions are used and we allow for many replications. Note that in the Shao's delete-d method, random samples are used to select the valiation data whereas in this method the whole partition is selected as random. This is acomplished using, `fold <- sample(rep(1:K,length=n))`. Then `fold` indicates each validation sample in the partition.

### 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

Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning. 2nd Ed. Springer-Verlag.

### See Also

`bestglm`, `CVd`, `CVDH`, `LOOCV`

### Examples

    #Example 1. 10-fold CV
    data(zprostate)
    train<-(zprostate[zprostate[,10],])[,-10]
    X<-train[,1:2]
    y<-train[,9]
    CVHTF(X,y,K=10,REP=1)[1]

[Package bestglm version 0.36 Index]