crqa: Auto, cross and multidimensional recurrence measures of one,...

In crqa: Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis

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)

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

Details

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

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

Note

Original bits of this code were translated from a Matlab version provided by Rick Dale, and created during the Non-Linear Methods for Psychological Science summer school held at the University of Cincinnati in 2012. The multi-dimensional method for the crqa function has been written together with Sebastian Wallot (sebastian.wallot at aesthetics.mpg.de )

Author(s)

Moreno I. Coco ([e-mail])

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

crqa documentation built on June 22, 2024, 9:17 a.m.