ZSCORE removes the mean and normalizes data to a variance of 1. Can be used for pre-whitening of data, too. [z,mu, sigma] = zscore(x [,OPT [, DIM]) z z-score of x along dimension DIM sigma is the inverse of the standard deviation mu is the mean of x The data x can be reconstucted with x = z*diag(sigma) + repmat(m, size(z)./size(m)) z = x*diag(1./sigma) - repmat(m.*v, size(z)./size(m)) OPT option 0: normalizes with N-1 [default] when computing sigma provides the square root of best unbiased estimator of the variance [1] 1: normalizes with N, when computing sigma this provides the square root of the second moment around the mean otherwise: best unbiased estimator of the standard deviation (see [1]) DIM dimension 1: STATS of columns 2: STATS of rows default or []: first DIMENSION, with more than 1 element see also: SUMSKIPNAN, MEAN, STD, DETREND REFERENCE(S): [1] http://mathworld.wolfram.com/z-Score.html
Package: nan