cov.wt  R Documentation 
Returns a list containing estimates of the weighted covariance matrix and the mean of the data, and optionally of the (weighted) correlation matrix.
## S4 method for signature 'fm' cov.wt(x, wt = rep(1/nrow(x), nrow(x)), cor = FALSE, center = TRUE, method = c("unbiased", "ML"))
x 
a matrix. 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

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 
method 
string specifying how the result is scaled, see 
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.
A list containing the following named components:
covthe estimated (weighted) covariance matrix.
centeran estimate for the center (mean) of the data.
n.obsthe number of observations (rows) in x
.
wtthe weights used in the estimation. Only returned if given as an argument.
corthe estimated correlation matrix. Only returned if cor
is
TRUE
mat < fm.runif.matrix(100, 10) cov.wt(mat, fm.runif(nrow(mat)))