This function estimate weights to apply to the rows of a data matrix to make the resulting weighted matrix as close to elliptically symmetric as possible.

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`formula` |
A one-sided or two-sided formula. The right hand side is used to define the design matrix. |

`data` |
An optional data frame. |

`subset` |
A list of cases to be used in computing the weights. |

`na.action` |
The default is na.fail, to prohibit computations. If set to na.omit, the function will return a list of weights of the wrong length for use with dr. |

`nsamples` |
The weights are determined by random sampling from a data-determined normal distribution. This controls the number of samples. The default is 10 times the number of cases. |

`sigma` |
Scale factor, set to one by default; see the paper by Cook and Nachtsheim for more information on choosing this parameter. |

`...` |
Arguments are passed to |

The basic outline is: (1) Estimate a mean m and covariance matrix S using a
possibly robust method; (2) For each iteration, obtain a random vector
from N(m,sigma*S). Add 1 to a counter for observation i if the i-th row
of the data matrix is closest to the random vector; (3) return as weights
the sample faction allocated to each observation. If you set the keyword
`weights.only`

to `T`

on the call to `dr`

, then only the
list of weights will be returned.

Returns a list of *n* weights, some of which may be zero.

Sanford Weisberg, sandy@stat.umn.edu

R. D. Cook and C. Nachtsheim (1994), Reweighting to achieve elliptically contoured predictors in regression. Journal of the American Statistical Association, 89, 592–599.

`dr`

, `cov.rob`

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