Description Usage Arguments Value Author(s) References See Also Examples

The functions `cooks.distance.mlm`

and `hatvalues.mlm`

are designed as extractor functions for regression deletion
diagnostics for multivariate linear models following Barrett & Ling (1992).
These are close analogs of
methods for univariate and generalized linear models handled by the
`influence.measures`

in the `stats`

package.

In addition, the functions provide diagnostics for deletion of
subsets of observations of size `m>1`

.

1 2 3 4 5 | ```
## S3 method for class 'mlm'
cooks.distance(model, infl = mlm.influence(model, do.coef = FALSE), ...)
## S3 method for class 'mlm'
hatvalues(model, m = 1, infl, ...)
``` |

`model` |
A |

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

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

`infl` |
An influence structure, of class |

`...` |
Other arguments, passed on |

When `m=1`

, these functions return a vector, corresponding to the observations
in the data set.

When `m>1`

, they return a list of *m \times m* matrices,
corresponding to deletion of subsets of size `m`

.

Michael Friendly

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.

`influencePlot.mlm`

, ~~~

1 2 3 4 5 6 7 |

mvinfluence documentation built on April 8, 2018, 3 a.m.

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