Description Usage Arguments Details Value Notes See Also
ccf_errors
returns information on the undertainty on CCF estimates.
1 2 3 4 |
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 |
(array) list of lags at which the CCF is to be evaluated. |
min.pts |
(integer) each DCF bin must contain at least |
local.est |
(logical) use 'local' (not 'global') means and variances? |
cov |
(logical) if |
prob |
(logical) probability level to use for confidence intervals |
nsim |
(integer) number of FR/RSS simulations to run |
peak.frac |
(float) only include CCF points above |
zero.clip |
(logical) remove pairs of points with exactly zero lag? |
one.way |
(logical) (ICCF only) if TRUE then only interpolar time series 2. |
method |
(string) use |
use.errors |
(logical) if |
acf.flag |
(logical) |
chatter |
(integer) set the level of feedback. |
Computes errors on the CCF estimates using "flux randomisation"
and "random subset sampling" FR/RSS using the fr_rss
function.
The output is a list containing two data frames: lags
and dists
.
lags |
a data frame with four columns |
tau |
time lags |
dcf |
the DCF values for the input data |
lower |
the lower limit of the confidence interval |
upper |
the upper limit of the confidence interval |
dists |
a data frame with two columns |
peak.lag |
the peak values from nsim simulations |
cent.lag |
the centroid values from nsim simulations |
For each randomised pair of light curves we compute the CCF. We record the
CCF, the lag at the peak, and the centroid lag (including only points higher
than peak.frac * max(ccf)
). Using nsim
simulations we compute
the (1-p)*100%
confidence intervals on the CCF values, and the
distribution of the peaks and centroids.
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