crqa (version 2.0.3)

crqa: Auto, cross and multidimensional recurrence measures of one, two or multiple time-series, time-delayed and embedded in higher dimensional space

Description

Core recurrence function, which examines recurrent structures of a single (rqa), two (crqa), or multidimensional time-series (mdcrqa), which are time-delayed and embedded in higher dimensional space. The approach compares the phase space trajectories of the time-series in the same phase-space when delays are introduced. A distance matrix between the time-series, delayed and embedded is calculated. Several measures representative of the underlying dynamics of the system are extracted (explained below).

Usage

crqa(ts1, ts2, delay, embed, rescale, radius, normalize,
mindiagline, minvertline, tw, whiteline, recpt, side, method,
metric, datatype)

Value

If a recurrence plot (RP) can be calculated and hence recurrence observed the function will returna a list with different measures extracted. Otherwise, the values for the output arguments will be either 0 or NA.

RR

The percentage of recurrent points falling within the specified radius (range between 0 and 100)

DET

Proportion of recurrent points forming diagonal line structures.

NRLINE

The total number of lines in the recurrent plot

maxL

The length of the longest diagonal line segment in the plot, excluding the main diagonal

L

The average length of line structures

ENTR

Shannon information entropy of diagonal line lengths longer than the minimum length

rENTR

Entropy measure normalized by the number of lines observed in the plot. Handy to compare across contexts and conditions

LAM

Proportion of recurrent points forming vertical line structures

TT

The average length of vertical line structures

catH

Entropy of categorical recurrence plots based on rectangular block structures

RP

The Recurrence Plot sparse matrix data

Arguments

ts1

First time-series dataset.

ts2

Second time-series dataset

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 = 0 (do nothing); if rescale = 1 (mean distance of entire matrix); if rescale = 2 (maximum distance of entire matrix). if rescale = 3 (minimum distance of entire matrix). if rescale = 4 (euclidean 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 Theiler window parameter

whiteline

A logical flag to calculate (TRUE) or not (FALSE) empty vertical lines.

recpt

A logical flag indicating whether measures of cross-recurrence are calculated directly from a recurrent plot (TRUE) or not (FALSE).

side

A string indicating whether recurrence measures should be calculated in the 'upper' triangle of the RP 'lower' triangle of the matrix, or 'both'. LOC is automatically excluded for 'upper' and 'lower'.

method

A string to indicate the type of recurrence analysis to peform. There are three options: rqa (autorecurrence); crqa(cross-recurrence); mdcrqa(multidimensional recurrence). Default value is crqa

metric

A string to indicate the type of distance metric used, default is euclidean but see help rdist() to list all other possible metrics.

datatype

a string (continuous or categorical) to indicate whether the nature of the data type

Author

Moreno I. Coco ([e-mail])

Details

We recommend setting whiteline = FALSE, as the current version of the library does not make use of such information to extract recurrence measures.

References

Coco, M. I., and Dale, R. (2014). Cross-recurrence quantification analysis of categorical and continuous time series: an R package. Frontiers in psychology, 5, 510.

Wallot, S. (2018). Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) a method for quantifying correlation between multivariate time-series. Multivariate behavioral research, 1-19

See Also

spdiags, simts

Examples

# use the available data
data(crqa) 

listener = eyemovement$listener
narrator = eyemovement$narrator

delay = 1; embed = 1; rescale = 0; radius = .1;
normalize = 0; mindiagline = 2; minvertline = 2;
tw = 0; whiteline = FALSE; recpt = FALSE; side = "both"
method = 'crqa'; metric = 'euclidean';  
datatype = "categorical"

ans = crqa(narrator, listener, delay, embed, rescale, radius, normalize, 
           mindiagline, minvertline, tw, whiteline, recpt, side, method,
           metric, datatype)

print(ans[1:10]) ## last argument of list is the recurrence plot