cov.wt
Weighted Covariance Matrices
Returns a list containing estimates of the weighted covariance matrix and the mean of the data, and optionally of the (weighted) correlation matrix.
 Keywords
 multivariate
Usage
cov.wt(x, wt = rep(1/nrow(x), nrow(x)), cor = FALSE, center = TRUE, method = c("unbiased", "ML"))
Arguments
 x
 a matrix or data frame. As usual, rows are observations and columns are variables.
 wt
 a nonnegative and nonzero vector of weights for each
observation. Its length must equal the number of rows of
x
.  cor
 a logical indicating whether the estimated correlation weighted matrix will be returned as well.
 center
 either a logical or a numeric vector specifying the
centers to be used when computing covariances. If
TRUE
, the (weighted) mean of each variable is used, ifFALSE
, zero is used. Ifcenter
is numeric, its length must equal the number of columns ofx
.  method
 string specifying how the result is scaled, see ‘Details’ below. Can be abbreviated.
Details
By default, method = "unbiased"
,
The covariance matrix is divided by one minus the sum of squares of
the weights, so if the weights are the default ($1/n$) the conventional
unbiased estimate of the covariance matrix with divisor $(n  1)$
is obtained. This differs from the behaviour in SPLUS which
corresponds to method = "ML"
and does not divide.
Value

A list containing the following named components:
 cov
 the estimated (weighted) covariance matrix
 center
 an estimate for the center (mean) of the data.
 n.obs
 the number of observations (rows) in
x
.  wt
 the weights used in the estimation. Only returned if given as an argument.
 cor
 the estimated correlation matrix. Only returned if
cor
isTRUE
.
See Also
Examples
library(stats)
(xy < cbind(x = 1:10, y = c(1:3, 8:5, 8:10)))
w1 < c(0,0,0,1,1,1,1,1,0,0)
cov.wt(xy, wt = w1) # i.e. method = "unbiased"
cov.wt(xy, wt = w1, method = "ML", cor = TRUE)
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