CVd {bestglm} | R Documentation |

## Cross-validation using delete-d method.

### Description

The delete-d method for cross-validation uses a random sample of d observations as the validation sample. This is repeated many times.

### Usage

```
CVd(X, y, d = ceiling(n * (1 - 1/(log(n) - 1))), REP = 100, family = gaussian, ...)
```

### Arguments

`X` |
training inputs |

`y` |
training output |

`d` |
size of validation sample |

`REP` |
number of replications |

`family` |
glm family |

`...` |
optional arguments passed to |

### Details

Shao (1993, 1997) suggested the delete-d algorithm implemented in this function.
In this algorithm, a random sample of d observations are taken as the validation
sample.
This random sampling is repeated `REP`

times.
Shao (1997, p.234, eqn. 4.5 and p.236) suggests `d= n(1-1/(log n - 1))`

,
This is obtained by taking `\lambda_n = log n`

on page 236 (Shao, 1997).
As shown in the table Shao's recommended choice of the d parameter corresponds
to validation samples that are typically much larger that used in 10-fold or
5-fold
cross-validation. LOOCV corresponds to d=1 only!

n | d | K=10 | K=5 |

50 | 33 | 5 | 10 |

100 | 73 | 10 | 20 |

200 | 154 | 20 | 40 |

500 | 405 | 50 | 100 |

1000 | 831 | 100 | 200 |

### 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

Shao, Jun (1993). Linear Model Selection by Cross-Validation. Journal of the American Statistical Assocation 88, 486-494.

Shao, Jun (1997). An Asymptotic Theory for Linear Model Selection. Statistica Sinica 7, 221-264.

### See Also

### Examples

```
#Example 1. delete-d method
#For the training set, n=67. So 10-fold CV is like using delete-d
#with d=7, approximately.
data(zprostate)
train<-(zprostate[zprostate[,10],])[,-10]
X<-train[,1:2]
y<-train[,9]
set.seed(123321123)
CVd(X, y, d=7, REP=10)
#should set to 1000. Used 10 to save time in example.
```

*bestglm*version 0.37.3 Index]