corrfun

Compute the autocorrelation or cross-correlation function.

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Usage

corrfun(xvector {, n {, scale}})	... autocorrelation of x
corrfun(xvector, yvector {, n {, scale}})	... cross correlation of x and y

x, y

data vectors

n

the number of lags; 0 < n < ncol(x) (default=ncol(x)/2)

scale

specifies the scaling of the function:

scale=0	... no scaling (default)	scale=1	... "biased", each lag i is scaled by the length of x (1/ncol(x))	scale=2	... "unbiased", each lag i is scaled by the number of correlated elements (1/(ncol(x)-1)) Result 1 The correlecation coefficient r (product-moment correlation) of the vectors x and y. r = var(x, y) / sqrt( var(x) * var(y) ) Usage 1 var(xvector, yvector) Result 1 The correlecation coefficient r (product-moment correlation) of the vectors x and y. r = var(x, y) / sqrt( var(x) * var(y) ) Usage 2 var(xmatrix) var(xmatrix, yscalar) var(xmatrix, yvector) Result 2 The correlation matrix r of the column vectors of x. r[i,j] = var(x[*,i], x[*,j]) / sqrt( var(x[*,i]) * var(x[*,j]) ) , with: i,j = 0..ncol(x)-1 If the argument y is supplied, it is used as column average like for the computation of the covariance matrix. See also avr, dev, var, corrfun <function list> corrfun corrfun(x, lags, scale) lags the number of coefficients def.=nrow(x)/2 scale the scaling def.=0 0=no scaling 1=scaling with "1 / nrow(x)", 2=scaling with "1 / (nrow(x) - lag)" The result is a vector with lags elements.
corrfun(x, y, lags, scale)	Calculate the coefficients 0..lags-1 of the cross-correlation function of the vectors x and y. The parameters are the same as for the auto-correlation function.