dr.weights {dr} | R Documentation |
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.
dr.weights(formula, data = list(), subset, na.action = na.fail, sigma=1, nsamples=NULL, ...)
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.
data(ais) w1 <- dr.weights(~ Ht +Wt +RCC, data = ais) m1 <- dr(LBM~Ht+Wt+RCC,data=ais,weights=w1)