Programmer Guide/Command Reference/EVAL/corrfun: Difference between revisions
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::{|class="keinrahmen" | ::{|class="keinrahmen" | ||
|''cyclic=0'' | |''cyclic=0'' | ||
| ... normal | | ... normal (default); (<code>acf[i]=sum(x[t]*y[(t+i)%ncol(x)], t=0..ncol(x)-1-i)<code>) | ||
|- | |- | ||
|''cyclic!=0'' | |''cyclic!=0'' | ||
| ... cyclic | | ... cyclic; (<code>acf[i]=sum(x[t]*y[(t+i)%ncol(x)], t=0..ncol(x)-1)<code>) | ||
|} | |} | ||
Revision as of 09:37, 1 September 2023
Compute the autocorrelation or cross-correlation function.
- Usage
corrfun(xvector {, n {, scale {, cyclic}}})... autocorrelation of x corrfun(xvector, yvector {, n {, scale {, cylic}}})... 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)-i))
- cyclic
- normal or cyclic indexing
cyclic=0 ... normal (default); ( acf[i]=sum(x[t]*y[(t+i)%ncol(x)], t=0..ncol(x)-1-i))cyclic!=0 ... cyclic; ( acf[i]=sum(x[t]*y[(t+i)%ncol(x)], t=0..ncol(x)-1))
- Result
- The autocorrelation of the data vector x or the cross correlation function of the vectors x and y. The result is a scalar (if n=1) or a vector with n elements.
- See also
- corr
