dr.weights {dr} R Documentation

## Estimate weights for elliptical symmetry

### Description

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.

### Usage

```dr.weights(formula, data = list(), subset, na.action = na.fail,
sigma=1, nsamples=NULL, ...)

```

### Arguments

 `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 `cov.rob` to compute a robust estimate of the covariance matrix.

### Details

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.

### Value

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

### Author(s)

Sanford Weisberg, sandy@stat.umn.edu

### References

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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