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 |

### 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

### 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]
```

*bestglm*version 0.37.3 Index]