Description Usage Arguments Value Notes See Also Examples
iccf
returns the Interpolated Cross-Correlation Function estimates.
1 2 |
ts.1 |
(array or dataframe) data for time series 1 and 2. |
ts.2 |
(array or dataframe) data for time series 1 and 2. |
tau |
(vector) list of lags at which to compute the CCF. |
local.est |
(logical) use 'local' (not 'global') means and variances? |
one.way |
(logical) (ICCF only) if TRUE then only interpolar time series 2. |
zero.clip |
(logical) remove pairs of points with exactly zero lag? |
cov |
(logical) if |
chatter |
(integer) set the level of feedback. |
A data frame containing columns:
tau |
(array) lags (in time units) |
ccf |
(array) correlations coefficent in each lag bin |
n |
(array) A one dimensional array containing the number of pairs of points used at each lag. |
In what follows we refer to the t, y
values of time series 1
(ts.1
) as t.1, y.1
, and similarly for time series 2.
Given two time series y.1
and y.2
, sampled at times t.1
and t.2
, we estimate a cross correlation function (CCF) by
interpolating (t.2, y.2
). For a lag tau
we estimate y.2
at each time t.1+tau
by interpolating between the two nearest points
of y.2
. We then pair the values y.1
with the corresponding
lagged values of y.2
and compute the linear correlation coefficient,
ccf
. The interpolation is handled by the approx
function for
linear interpolation.
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