| 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 non-negative and non-zero 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 S-PLUS 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)))