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nan_zscore

removes the mean and normalizes the data to a variance of 1.

Calling Sequence

[z,r,m] = nan_zscore(x,DIM)

Parameters

x :

data

DIM:

dimension 1 - STATS of columns 2 - STATS of rows default or []- first DIMENSION, with more than 1 element

z:

z-score of x along dimension DIM

r:

is the inverse of the standard deviation

m:

is the mean of x

Description

Can be used for Pre-Whitening of the data, too.

The data x can be reconstrated with

x = z*diag(1./r) + repmat(m,size(z)./size(m))

z = x*diag(r) - repmat(m.*v,size(z)./size(m))

See also

Bibliography

[1] http://mathworld.wolfram.com/z-Score.html

Authors


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