robust (version 0.3-19)

ccov: Classical Covariance Estimation

Description

Computes 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

ccov(data, corr = FALSE, center = TRUE, distance = TRUE,
    na.action = na.fail, unbiased = TRUE, control = list())

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.

control

not used, but necessarily allowed in order to fit the fit.models framework.

Value

• an object of class "cov" with components:
• 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 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.

See Also

covRob, var, cov.wt.

Examples

data(stack.dat)
  ccov(stack.dat)