SurrogateData function

Simulation of surrogates for a given time series x, subject to the specified method and parameters

It simulates a surrogate for the time series x to be analyzed by wavelet transformation using either function analyze.wavelet or function analyze.coherency. A set of surrogates is used for significance assessment to test the hypothesis of equal periodic components.

Simulation is subject to model/method specification and parameter setting: Currently, one can choose from a variety of 6 methods (white noise, series shuffling, Fourier randomization, AR, and ARIMA) with respective lists of parameters to set.

The name and layout were inspired by a similar function developed by Huidong Tian (archived R package WaveletCo).

SurrogateData(x, method = "white.noise", params = list( 
         AR    = list(p = 1), 
         ARIMA = list(p = 1, q = 1, include.mean = TRUE, sd.fac = 1, 
                      trim = FALSE, trim.prop = 0.01)))

Arguments

• x: the given time series

• method: the method of generating surrogate time series; select from:

  	"white.noise"	:	white noise
  	"shuffle"	:	shuffling the given time series
  	"Fourier.rand"	:	time series with a similar spectrum
  	"AR"	:	AR(p)
  	"ARIMA"	:	ARIMA(p,0,q)

  Default: "white.noise".

• params: a list of assignments between methods (AR, and ARIMA) and lists of parameter values applying to surrogates. Default: NULL.

  Default includes:

  AR = list(p = 1),

  where:

  	p	:	AR order

  ARIMA = list(p = 1, q = 1, include.mean = TRUE, sd.fac = 1,

  trim = FALSE, trim.prop = 0.01),

  where:

  	p	:	AR order
  	q	:	MA order
  	include.mean	:	Include a mean/intercept term?
  	sd.fac	:	magnification factor to boost the
  			residual standard deviation
  	trim	:	Simulate trimmed data?
  	trim.prop	:	high/low trimming proportion

Returns

A surrogate series for x is returned which has the same length and properties according to estimates resulting from the model/method specification and parameter setting.

References

Tian, H., and Cazelles, B., 2012. WaveletCo. Available at https://cran.r-project.org/src/contrib/Archive/WaveletCo/, archived April 2013; accessed July 26, 2013.

Author(s)

Angi Roesch and Harald Schmidbauer; credits are also due to Huidong Tian.

See Also

analyze.wavelet, analyze.coherency, AR, ARIMA, FourierRand