R: Simulation of surrogates for a given time series x, subject...

SurrogateData {WaveletComp}

R Documentation

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

Description

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).

Usage

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

Value

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.

Author(s)

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

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.