cov {robust} | R Documentation |
Computes an estimate of the covariance/correlation matrix and location vector using classical methods.
cov(data, corr = FALSE, center = TRUE, distance = TRUE, na.action = na.fail, unbiased = TRUE)
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 unbiased = TRUE then an unbiased estimate of the covariance matrix is computed. If unbiased = FALSE then a maximum likelihood estimate is computed. |
This function is intended to produce an object similar to that produced by the covRob
in the robust library but fit using classical methods.
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 . |
data(stack.dat) cov(stack.dat)