It computes cross-recurrence is calculated in overlapping
windows of a certain size for a number of delays smaller
than the size of the window. For each window,
a cross-recurrence plot is build and measures of it extracted.
The number of embedding dimension for
phase-reconstruction, i.e., the lag intervals.
rescale
Rescale the distance matrix;
if rescale = 1 (mean distance of entire matrix);
if rescale = 2 (maximum distance of entire matrix).
radius
A threshold, cut-off, constant used to
decide whether two points are recurrent or not.
normalize
Normalize the time-series;
if normalize = 0 (do nothing);
if normalize = 1 (Unit interval);
if normalize = 2 (z-score).
mindiagline
A minimum diagonal length of
recurrent points. Usually set to 2, as it takes
a minimum of two points to define any line.
minvertline
A minimum vertical length of
recurrent points.
tw
The size of the Theiler window
whiteline
A logical flag to calculate (TRUE)
or not (FALSE) empty vertical lines.
trend
A logical flag indicating whether
the TREND should be computed
Value
It returns a matrix where the rows are the
different windows explored, and the columns
are the cross-recurrence measures observed
in that particular window.
Refer to crqa for the values returned.
Note
If no-recurrence is found in a window,
that window will not be saved, and a message
about it will be warned.
TREND is implemented following a solution proposed
by Norbert Marwan, and translated here in R,
for those who have asked him.
He, however warns that this measure might strongly depend
on the chosen settings to calculate crq.
Relying on such measure can, therefore, produce misleading results.
Author(s)
Moreno I. Coco (moreno.cocoi@gmail.com)
See Also
crqa
Examples
## simulate two dichotomous series
tS = simts(0.25, 0.05, 0.2, 0.2, 0.25, 100)
ts1 = tS[1,]; ts2 = tS[2,]
## check data(crqa) for alternative data
## (e.g., RDts1, RDts2)
windowstep = 10; windowsize = 50;
delay = 1; embed = 1 ; rescale = 1;
radius = 0.00001; normalize = 0;
minvertline = 2; mindiagline = 2;
tw = 0; whiteline = FALSE; trend = TRUE;
## it returns a list with:
## [[1]] the measures for the different windows where values are found
## [[2]] the trend over time.
res = wincrqa(ts1, ts2, windowstep, windowsize,
delay, embed, rescale, radius, normalize, mindiagline,
minvertline, tw, whiteline, trend)
str(res)