R: Plot (approximation) to the theoretical power of the...

genwwn.powerplot {hwwntest}

R Documentation

Plot (approximation) to the theoretical power of the `genwwn.test` test for ARMA processes (including, of course, white noise itself) for a range of sample sizes.

Description

Computes and plots (approximation) to the theoretical power of the genwwn.test test using the genwwn.thpower function.

Usage

genwwn.powerplot(N =c(32, 64, 128, 256, 512, 1024), ar = NULL,
	ma = NULL, plot.it = TRUE, sigsq = 1, alpha = 0.05,
	away.from = "standard", filter.number = 10,
	family = "DaubExPhase", verbose = FALSE, ylim=c(0,1))

Arguments

`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 `ar` argument except for MA terms. A vector of length q for MA(q) parameters, with first entry being beta_1, the second being beta_2, etc. If this argument is NULL then there are no MA terms.
`plot.it`	If `TRUE` then a plot of theoretical power against sample size is produced. The computed theoretical powers for the fixed sample sizes specified by `N` are plotted as crosses. The crosses are then joined by a dashed line to indicate a likely trajectory of the theoretical power for sample sizes not computed.
`sigsq`	The theoretical innovation variance (also the variance of white noise if `ar=ma=NULL`.
`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 `genwwn.test`. This can be an integer up to the number of scales. However, mostly you can leave this at "standard" where the scales calculation is automatically determined.
`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 `alpha`=0.05 then the theoretical powers would all be approximately 0.05.)

Details

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.

Value

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 `away.from` argument supplied
`filter.number`	The wavelet filter number used
`family`	The wavelet family used

Author(s)

Delyan Savchev and Guy Nason

References

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

Examples

#
# Plot theoretical power for white noise
#
genwwn.powerplot()
#
# Plot theoretical power for AR(1) process
#
genwwn.powerplot(ar=0.8)

[Package hwwntest version 1.3.2 Index]

Plot (approximation) to the theoretical power of the genwwn.test test for ARMA processes (including, of course, white noise itself) for a range of sample sizes.