criticalvaluesWSP: Estimates critical values for Wavelet spectra

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/sowas.R

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

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  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.