CVbinary {DAAG} | R Documentation |

## Cross-Validation for Regression with a Binary Response

### Description

These functions give training (internal) and cross-validation measures of predictive accuracy for regression with a binary response. The data are randomly divided between a number of ‘folds’. Each fold is removed, in turn, while the remaining data are used to re-fit the regression model and to predict at the omitted observations.

### Usage

```
CVbinary(obj, rand=NULL, nfolds=10, print.details=TRUE)
cv.binary(obj, rand=NULL, nfolds=10, print.details=TRUE)
```

### Arguments

`obj` |
a |

`rand` |
a vector which assigns each observation to a fold |

`nfolds` |
the number of folds |

`print.details` |
logical variable (TRUE = print detailed output, the default) |

### Value

`cvhat` |
predicted values from cross-validation |

`internal` |
internal or (better) training predicted values |

`training` |
training predicted values |

`acc.cv` |
cross-validation estimate of accuracy |

`acc.internal` |
internal or (better) training estimate of accuracy |

`acc.training` |
training estimate of accuracy |

### Note

The term ‘training’ seems preferable to the term ‘internal’ in connection with predicted values, and the accuracy measure, that are based on the observations used to derive the model.

### Author(s)

J.H. Maindonald

### See Also

### Examples

```
frogs.glm <- glm(pres.abs ~ log(distance) + log(NoOfPools),
family=binomial,data=frogs)
CVbinary(frogs.glm)
mifem.glm <- glm(outcome ~ ., family=binomial, data=mifem)
CVbinary(mifem.glm)
```

*DAAG*version 1.25.6 Index]