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

View source: R/genwwn.powerplot.R

Computes and plots (approximation) to the theoretical power of the
`genwwn.test`

test using the
`genwwn.thpower`

function.

1 2 3 4 |

`N` |
Vector of lengths of the series you want to plot theoretical power for. |

`ar` |
Autoregressive parameters. A vector with p entries for AR(p) with the first entry being the value for lag-one term (alpha_1), the second entry being the value for the lag-two term (alpha_2) etc. If this argument is NULL then there are no AR terms. |

`ma` |
Similar to the |

`plot.it` |
If |

`sigsq` |
The theoretical innovation variance (also the variance
of white noise if |

`alpha` |
The nominal size of the test for this theoretical power calculation. |

`away.from` |
Describes how many fine scales to exclude, the
same as in |

`filter.number` |
The number of vanishing moments in the Daubechies series of wavelets. |

`family` |
The wavelet family. |

`verbose` |
If TRUE then informative messages are printed during the progress of the function. |

`ylim` |
The theoretical power is a probability and lies in the range
of zero to one and this argument specifies those limits for the
vertical axis. These can be changed to whatever you like. E.g.
if all the powers were similar (e.g. if the null series was white
noise and |

Function calculates the value of the power function at
the specified sample sizes using the
`genwwn.thpower`

function. Then these values
are plotted and returned.

A list containing the following components.

`N` |
The vector of sample sizes. |

`power` |
The computed theoretical powers for each sample size |

`ar` |
The autoregressive parameters, NULL if there are none. |

`ma` |
The moving average parameters, NULL if there are none. |

`sigsq` |
The innovation variance used |

`alpha` |
The significance level used |

`away.from` |
The value of the |

`filter.number` |
The wavelet filter number used |

`family` |
The wavelet family used |

Delyan Savchev and Guy Nason

Nason, G.P. and Savchev, D. (2014) White noise testing using
wavelets. *Stat*, **3**, 351-362.
http://dx.doi.org/10.1002/sta4.69

1 2 3 4 5 6 7 8 | ```
#
# Plot theoretical power for white noise
#
## Not run: genwwn.powerplot()
#
# Plot theoretical power for AR(1) process
#
## Not run: genwwn.powerplot(ar=0.8)
``` |

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