cv.lm {DAAG} | R Documentation |

## Cross-Validation for Linear Regression

### Description

This function gives internal and cross-validation measures of predictive
accuracy for ordinary linear regression. The data are
randomly assigned to a number of `folds'.
Each fold is removed, in turn, while the remaining data is used
to re-fit the regression model and to predict at the deleted observations.

### Usage

cv.lm(df = houseprices, form.lm = formula(sale.price ~ area), m=3, dots =
FALSE, seed=29, plotit=TRUE, printit=TRUE)

### Arguments

`df` |
a data frame |

`form.lm` |
a formula object |

`m` |
the number of folds |

`dots` |
uses pch=16 for the plotting character |

`seed` |
random number generator seed |

`plotit` |
if TRUE, a plot is constructed on the active device |

`printit` |
if TRUE, output is printed to the screen |

### Value

For each fold, a table listing

the residuals

ms = the overall mean square of prediction error

### Author(s)

J.H. Maindonald

### See Also

`lm`

### Examples

cv.lm()

[Package

*DAAG* version 0.76

Index]