R: Weighted Covariance Matrices

cov.wt {base} | R Documentation

## Weighted Covariance Matrices

### Description

Returns a list containing estimates of the weighted covariance matrix and the mean of the data, and optionally of the (weighted) correlation matrix.

### Usage

    cov.wt(x, wt = rep(1/nrow(x), nrow(x)), cor = FALSE, center = TRUE)

### Arguments

`x` | a matrix or data frame. 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 `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, if `FALSE`, zero is used. If `center` is numeric, its length must equal the number of columns of `x`.

### Details

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.

### 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` is `TRUE`.

### See Also

`cov` and `var`.

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