RobStatTM (version 1.0.8)

covClassic: Classical Covariance Estimation

Description

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

Usage

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

Value

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

center

a numeric vector containing the estimate of the location vector.

cov

a numeric matrix containing the estimate of the covariance matrix.

cor

a numeric matrix containing the estimate of the correlation matrix if the argument corr = TRUE. Otherwise it is set to NULL.

dist

a numeric vector containing the squared Mahalanobis distances. Only present if distance = TRUE in the call.

call

an image of the call that produced the object with all the arguments named. The matched call.

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

a logical flag. If TRUE the unbiased estimator is returned (computed with denominator equal to n-1), else the MLE (computed with denominator equal to n) is returned.

Details

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

Examples

data(wine)
round( covClassic(wine)$cov, 2)