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# covm

## PURPOSE

COVM generates covariance matrix

## SYNOPSIS

function [CC,NN] = covm(X,Y,Mode);

## DESCRIPTION

``` COVM generates covariance matrix
X and Y can contain missing values encoded with NaN.
NaN's are skipped, NaN do not result in a NaN output.
The output gives NaN only if there are insufficient input data

COVM(X,Mode);
calculates the (auto-)correlation matrix of X
COVM(X,Y,Mode);
calculates the crosscorrelation between X and Y

Mode = 'M' minimum or standard mode [default]
C = X'*X; or X'*Y correlation matrix

Mode = 'E' extended mode
C = [1 X]'*[1 X]; % l is a matching column of 1's
C is additive, i.e. it can be applied to subsequent blocks and summed up afterwards
the mean (or sum) is stored on the 1st row and column of C

Mode = 'D' or 'D0' detrended mode
the mean of X (and Y) is removed. If combined with extended mode (Mode='DE'),
the mean (or sum) is stored in the 1st row and column of C.
The default scaling is factor (N-1).
Mode = 'D1' is the same as 'D' but uses N for scaling.

C = covm(...);
C is the scaled by N in Mode M and by (N-1) in mode D.
[C,N] = covm(...);
C is not scaled, provides the scaling factor N
C./N gives the scaled version.```

## CROSS-REFERENCE INFORMATION

This function calls:
This function is called by:
• mvar MVAR estimates Multi-Variate AutoRegressive model parameters

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