locits (version 1.4)

ewspecHaarNonPer: 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

Value

• The same value as for the ewspec function.

Details

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

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.

See Also

hwtos2, HwdS

Examples

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