covClassic: Classical Covariance Estimation

In robust: Port of the S+ "Robust Library"

Description

Compute an estimate of the covariance/correlation matrix and location vector using classical methods.

Its main intention is to return an object compatible to that produced by covRob, but fit using classical methods.

Usage

covClassic(data, corr = FALSE, center = TRUE, distance = TRUE,
           na.action = na.fail, unbiased = TRUE, ...)

Arguments

data	a numeric matrix or data frame containing the data.
corr	a logical flag. If corr = TRUE then the estimated correlation matrix is computed.
center	a logical flag or a numeric vector of length p (where p is the number of columns of x) specifying the center. If center = TRUE then the center is estimated. Otherwise the center is taken to be 0.
distance	a logical flag. If distance = TRUE the Mahalanobis distances are computed.
na.action	a function to filter missing data. The default na.fail produces an error if missing values are present. An alternative is na.omit which deletes observations that contain one or more missing values.
unbiased	logical indicating if an unbiased estimate of the covariance matrix is should becomputed. If false, the maximum likelihood estimate is computed.
...	additional .

Value

a list with class “covClassic” containing the following elements:

call	an image of the call that produced the object with all the arguments named.
cov	a numeric matrix containing the estimate of the covariance/correlation matrix.
center	a numeric vector containing the estimate of the location vector.
dist	a numeric vector containing the squared Mahalanobis distances. Only present if distance = TRUE in the call.
corr	a logical flag. If corr = TRUE then cov contains an estimate of the correlation matrix of x.

Note

Originally, and in S-PLUS, this function was called cov; it has been renamed, as that did mask the function in the standard package stats.

See Also

covRob, var, cov.wt.

Examples

  data(stack.dat)
  covClassic(stack.dat)

robust documentation built on May 2, 2019, 5:20 p.m.