ewspecHaarNonPer: Compute evolutionary wavelet spectrum (EWS) estimate based on...

In locits: Test of Stationarity and Localized Autocovariance

Compute evolutionary wavelet spectrum (EWS) estimate based on the Haar wavelet transform.

Description

This function uses the special HwdS function to compute the Haar wavelet transform with out boundary conditions (neither periodic, interval, mirror reflection). This is so all coefficients are genuine Haar coefficients without involving extra/repeated data.

Usage

ewspecHaarNonPer(x, filter.number = 1, family = "DaubExPhase",
    UseLocalSpec = TRUE, DoSWT = TRUE, WPsmooth = TRUE,
    verbose = FALSE, smooth.filter.number = 10,
    smooth.family = "DaubLeAsymm",
    smooth.levels = 3:WPwst$nlevels - 1, smooth.dev = madmad,
    smooth.policy = "LSuniversal", smooth.value = 0,
    smooth.by.level = FALSE, smooth.type = "soft",
    smooth.verbose = FALSE, smooth.cvtol = 0.01,
    smooth.cvnorm = l2norm, smooth.transform = I,
    smooth.inverse = I)

Arguments

x	A vector of dyadic length that contains the time series you want to form the EWS of.
filter.number	Should always be 1 (for Haar)
family	Should always be "DaubExPhase", for Haar.
UseLocalSpec	Should always be TRUE.
DoSWT	Should always be TRUE
WPsmooth	Should alway be TRUE to do smoothing. If FALSE then not smoothed.
verbose	If TRUE informative messages are printed during the progress of the algorithm.
smooth.filter.number	Wavelet filter number for doing the wavelet smoothing of the EWS estimate.
smooth.family	Wavelet family for doing the wavelet smoothing of the EWS estimate.
smooth.levels	Which levels of the EWS estimate to apply smoothing to.
smooth.dev	What kind of deviance to use. The default is madmad, an alternative might be var.
smooth.policy	What kind of smoothing to use. See help page for ewspec
smooth.value	If a manual value has to be supplied according to the smooth.policy then this is it.
smooth.by.level	If TRUE then all levels are smoothed independently with different smoothing, otherwise all levels are smoothed together (eg one threshold for all levels).
smooth.type	The type of wavelet smoothing "hard" or "soft"
smooth.verbose	If TRUE then informative messages about the smoothing are printed.
smooth.cvtol	If cross-validation smoothing is used, this is the tolerance
smooth.cvnorm	If cross-validation smoothing used, this is the norm that's used
smooth.transform	A transform is applied before smoothing
smooth.inverse	The inverse transform is applied after smoothing

Details

This function is very similar to ewspec from wavethresh, and many arguments here perform the same function as there.

Value

The same value as for the ewspec function.

Author(s)

Guy Nason.

References

Nason, G.P. (2013) A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series. J. R. Statist. Soc. B, 75, 879-904. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssb.12015")}

See Also

hwtos2, HwdS

Examples

#
# Requires wavethresh, so not run directly in installation of package
#
ewspecHaarNonPer(rnorm(512))

locits documentation built on Sept. 8, 2023, 5:07 p.m.