criticalvaluesWSP {sowas} | R Documentation |

## Estimates critical values for Wavelet spectra

### 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 omega0 |

`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.

### Note

### Author(s)

D. Maraun

### References

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

### See Also

`wsp`

### Examples

##

[Package

*sowas* version 0.93

Index]