Programmer Guide/Command Reference/EVAL/corrfun: Difference between revisions
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:;''n'': the number of lags; 0 < n < <code>ncol(''x'')</code> (default=<code>ncol(''x'')/2</code>) | :;''n'': the number of lags; 0 < n < <code>ncol(''x'')</code> (default=<code>ncol(''x'')/2</code>) | ||
:;''scale'': specifies the scaling of the function: | :;''scale'': specifies the scaling of the function: | ||
::{|class=keinrahmen" | ::{|class="keinrahmen" | ||
|''scale=0'' | |''scale=0'' | ||
| ... no scaling (default) | | ... no scaling (default) | ||
Revision as of 14:26, 8 April 2011
Compute the autocorrelation or cross-correlation function.
- 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
- The autocorrelation of the data vector x or the cross correlation function of the two vector x and y. The result is a scalar (if n=1) or a vector with n elements.
- See also
- corr
