These functions implement the general classes of influence measures for multivariate regression models defined in Barrett and Ling (1992), Eqn 2.3, 2.4, as shown in their Table 1.

They are defined in terms of the submatrices for a deleted index subset *I*

*H_I = X_I (X^T X)^{-1} X_I*

*Q_I = E_I (E^T E)^{-1} E_I*

corresponding to the hat and residual matrices in univariate models.

For subset size *m = 1* these evaluate to scalar equivalents of
hat values and studentized residuals.

For subset size *m > 1* these are *m \times m* matrices and
functions in the *J^{det}* class use *|H_I|* and *|Q_I|*,
while those in the *J^{tr}* class use *tr(H_I)* and *tr(Q_I)*.

The functions `COOKD`

, `COVRATIO`

, and `DFFITS`

implement
some of the standard influence measures in these terms for the general
cases of multivariate linear models and deletion of subsets of size
`m>1`

, but they are only included here for experimental purposes.

1 2 3 4 5 6 7 8 9 |

`H` |
a scalar or |

`Q` |
a scalar or |

`a` |
the |

`b` |
the |

`f` |
scaling factor for the |

`n` |
sample size |

`p` |
number of predictor variables |

`r` |
number of response variables |

`m` |
deletion subset size |

These functions are purely experimental and not intended to be used directly. However, they may be useful to define other influence measures than are currently implemented here.

The scalar result of the computation.

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.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.