criticalvaluesWSP: Estimates critical values for Wavelet spectra

In Dasonk/SOWAS: Software for Wavelet Analysis and Synthesis

Description

This function estimates critical values for the Wavelet spectra by means of Monte Carlo simulations. Null Hypothesis is an AR1 (red noise) process fitted to the given time series.

Usage

  criticalvaluesWSP(ts, s0 = 1, noctave = 5, nvoice = 10,
    w0 = 2 * pi, swabs = 0, tw = 0, siglevel = 0.95,
    nreal = 1000)

Arguments

ts	time series object
s0	lowest calculated scale in units of the time series
noctave	number of octaves
nvoice	number of voices per octave
w0	time/frequency resolution \omega_0
swabs	length of smoothing window is 2swabs+1
tw	length of smoothing window in time direction is 2*s*tw+1
siglevel	significance level, e.g. 0.9, 0.95 or 0.99. siglevel might also be a vector, e.g. c(0.9,0.95) to plot more contourlines.
nreal	number of realizations to estimate critical values for the corresponding significance values, default 1000

Details

nreal might be chosen as 100 for a rough estimate of significance. However, it is for sure not suitable to reliably distinguish between 95 and 99 percent significance values. In this case, at least nreal=1000 should be chosen.

Value

Returns a matrix of scale DEPENDENT critical values. The number of rows of the matrix corresponds to the number of chosen significance values. The number of columns equals the number of scales.

Author(s)

D. Maraun

References

D. Maraun and J. Kurths, Nonlin. Proc. Geophys. 11: 505-514, 2004

See Also

wcoh

Examples

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Dasonk/SOWAS documentation built on May 6, 2019, 1:36 p.m.