crqa (version 1.0.6)

wincrqa: Window Cross-Recurrence Measures

Description

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.

Usage

wincrqa(ts1, ts2, windowstep, windowsize, delay, embed, rescale,
radius, normalize, mindiagline, minvertline, tw, whiteline, trend)

Arguments

ts1

First time-series

ts2

Second time-series

windowstep

Interval by which the window is moved.

windowsize

The size of the window

delay

The delay unit by which the series are lagged.

embed

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.

See Also

crqa

Examples

# NOT RUN {
## 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)

# }