rqa {nonlinearTseries}R Documentation

Recurrence Quantification Analysis (RQA)

Description

The Recurrence Quantification Analysis (RQA) is an advanced technique for the nonlinear analysis that allows to quantify the number and duration of the recurrences in the phase space.

Usage

rqa(
  takens = NULL,
  time.series = NULL,
  embedding.dim = 2,
  time.lag = 1,
  radius,
  lmin = 2,
  vmin = 2,
  distanceToBorder = 2,
  save.RM = TRUE,
  do.plot = FALSE,
  ...
)

Arguments

takens

Instead of specifying the time.series, the embedding.dim and the time.lag, the user may specify directly the Takens' vectors.

time.series

The original time series from which the phase-space reconstruction is performed.

embedding.dim

Integer denoting the dimension in which we shall embed the time.series.

time.lag

Integer denoting the number of time steps that will be use to construct the Takens' vectors.

radius

Maximum distance between two phase-space points to be considered a recurrence.

lmin

Minimal length of a diagonal line to be considered in the RQA. Default lmin = 2.

vmin

Minimal length of a vertical line to be considered in the RQA. Default vmin = 2.

distanceToBorder

In order to avoid border effects, the distanceToBorder points near the border of the recurrence matrix are ignored when computing the RQA parameters. Default, distanceToBorder = 2.

save.RM

Logical value. If TRUE, the recurrence matrix is stored as a sparse matrix. Note that computing the recurrences in matrix form can be computationally expensive.

do.plot

Logical. If TRUE, the recurrence plot is shown. However, plotting the recurrence matrix is computationally expensive. Use with caution.

...

Additional plotting parameters.

Value

A rqa object that consist of a list with the most important RQA parameters:

Author(s)

Constantino A. Garcia and Gunther Sawitzki

References

Zbilut, J. P. and C. L. Webber. Recurrence quantification analysis. Wiley Encyclopedia of Biomedical Engineering (2006).

Examples

## Not run: 
rossler.ts =  rossler(time=seq(0, 10, by = 0.01),do.plot=FALSE)$x
rqa.analysis=rqa(time.series = rossler.ts, embedding.dim=2, time.lag=1,
               radius=1.2,lmin=2,do.plot=FALSE,distanceToBorder=2)
plot(rqa.analysis)

## End(Not run)

[Package nonlinearTseries version 0.3.0 Index]