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COV Covariance Matrix

Section: Elementary Functions

# Usage

Computes the covariance of a matrix or a vector. The general syntax for its use is

      y = cov(x)

where `x` is a matrix or a vector. If `x` is a vector then `cov` returns the variance of `x`. If `x` is a matrix then `cov` returns the covariance matrix of the columns of `x`. You can also call `cov` with two arguments to compute the matrix of cross correlations. The syntax for this mode is

      y = cov(x,z)

where `x` and `z` are matrices of the same size. Finally, you can provide a normalization flag `d` that is either `0` or `1`, which changes the normalization factor from `L-1` (for `d=0`) to `L` (for `d=1`) where `L` is the number of rows in the matrix `x`. In this case, the syntaxes are

      y = cov(x,z,d)

for the two-argument case, and

      y = cov(x,d)

for the one-argument case.

# Example

The following demonstrates some uses of the `cov` function

    --> A = [5,1,3;3,2,1;0,3,1]

    A =
     5 1 3
     3 2 1
     0 3 1

    --> B = [4,-2,0;1,5,2;-2,0,1];

We start with the covariance matrix for `A`

    --> cov(A)

    ans =
        4.2222   -1.6667    1.5556
       -1.6667    0.6667   -0.6667
        1.5556   -0.6667    0.8889

and again with the (biased) normalization

    --> cov(A,1)

    ans =
        4.2222   -1.6667    1.5556
       -1.6667    0.6667   -0.6667
        1.5556   -0.6667    0.8889

Here we compute the cross covariance between `A` and `B`

    --> cov(A,B)

    ans =
        2.0988    1.6667
        1.6667    5.1111

and again with biased normalization

    --> cov(A,B,1)

    ans =
        2.0988    1.6667
        1.6667    5.1111