'drop1Wald' calculates tests for single term deletions based on the covariance matrix of estimated coefficients instead of re-fitting a reduced model. This helps in cases where re-fitting is not feasible, inappropriate or costly.

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`object` |
a fitted model of class 'regr'. |

`scope` |
a formula giving the terms to be considered for dropping. If 'NULL', 'drop.scope(object)' is obtained |

`scale` |
an estimate of the residual mean square to be used in computing Cp. Ignored if '0' or 'NULL'. |

`test` |
see |

`k` |
the penalty constant in AIC / Cp. |

`...` |
further arguments, ignored |

The test statistics and Cp and AIC values are calculated on the basis
of the estimated coefficients and their (unscaled) covariance matrix
as provided by the fitting object.
The function may be used for all model fitting objects that contain
these two components as `$coefficients`

and `$cov.unscaled`

.

An object of class 'anova' summarizing the differences in fit between the models.

drop1Wald is used in `regr`

for models of class 'lm' or
'lmrob' for preparing the 'testcoef' table.

Werner A. Stahel, Seminar for Statistics, ETH Zurich

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