# Calculate Regression Deletion Diagnostics for Multivariate Linear Models

### Description

`mlm.influence`

is the main computational function in this package.
It is usually not called directly, but rather via its alias,
`influence.mlm`

, the S3 method for a `mlm`

object.

### Usage

1 | ```
mlm.influence(model, do.coef = TRUE, m = 1, ...)
``` |

### Arguments

`model` |
An |

`do.coef` |
logical. Should the coefficients be returned in the |

`m` |
Size of the subsets for deletion diagnostics |

`...` |
Further arguments passed to other methods |

### Details

The computations and methods for the `m=1`

case are straight-forward,
as are the computations for the `m>1`

case. Associated methods for
`m>1`

are still under development.

### Value

`mlm.influence`

returns an S3 object of class `inflmlm`

, a list with the following components

`m` |
Deletion subset size |

`H` |
Hat values, |

`Q` |
Residuals, |

`CookD` |
Cook's distance values |

`L` |
Leverage components |

`R` |
Residual components |

`subsets` |
Indices of the observations in the subsets of size |

`labels` |
Observation labels |

`call` |
Model call for the |

`Beta` |
Deletion regression coefficients– included if |

### Author(s)

Michael Friendly

### References

Barrett, B. E. and Ling, R. F. (1992).
General Classes of Influence Measures for Multivariate Regression.
*Journal of the American Statistical Association*, **87**(417), 184-191.

Barrett, B. E. (2003). Understanding Influence in Multivariate Regression.
*Communications in Statistics – Theory and Methods*, **32**, 3, 667-680.

### See Also

`influencePlot.mlm`

, ~~~

### Examples

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