dr.estimate.weights: Compute estimated weighting toward normality

Description

This function estimate weights to apply to the rows of a data matrix to make the resulting weighted matrix as close to multivariate normality as possible. This method is usually not called directly by the user.

Usage

dr.estimate.weights(object, sigma=1, covmethod="mve", nsamples=NULL, ...)
robust.center.scale(x, method,... )

Arguments

object

a dimension reduction regression object name, or an n by p matrix

sigma

A tuning parameter, with default 1, usually in the range .2 to 1.0

covmethod

covmethod is passed as the argument method to the function cov.rob in the required package lqs. The choices are "classical", "mve" and "mcd". This probably will not wor

nsamples

The weights are determined by random sampling from a data-determined normal distribution. This controls the number of samples

An $n \times p$ data matrix with no missing values

method

see covmethod above

...

Additional args are passed to cov.rob

Value

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

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.

References