mvLSW: Multivariate Locally Stationary Wavelet Object

Description

Constructs a multivariate locally stationary wavelet (mvLSW) object.

Usage

mvLSW(spectrum, filter.number = 1, family = "DaubExPhase", smooth.type = "all", smooth.kernel = kernel("daniell", 0),  bias.correct = FALSE, min.eig.val = -Inf, names = NULL)  "is"(object)

Arguments

spectrum

4D array of order PxPxJxT where P is the number of signals of the time series of length T such that T=$2^J$ for some positive integer J defining the levels of the mvLSW object.

family

Specifies the family of wavelet. Only two options are available, either "DaubExPhase" (default) or "DaubLeAsymm".

filter.number

Integer number defining the number of vanishing moments of the wavelet function. By default, filter.number=1 and so defining the Haar wavelet.

smooth.type

What type of smoothing regime has been applied. Either "all" (default) if the smoothing method been applied to all levels. Otherwise "by.level", a different smoothing method is applied to each level.

smooth.kernel

Definition of the smoothing kernel from kernel(). By default, the identity kernel is defined.

bias.correct

Logical, has a bias correction been applied to the data. FALSE by default.

min.eig.val

Minimum eigenvalue from spectral matrices across all levels and locations, set at -Inf by default. If NA, then the minimum eigenvalue is calculated.

names

Character vector containing the signal names of the multivariate time series.

object

An R object.

Value

The mvLSW command invisibly returns a list with the following items:The list Information contains:The command is.mvLSW returns TRUE if the supplied object is a mvLSW object as described above. Otherwise, the command returns FALSE.

Details

Construction of a multivariate locally stationary classed object that contains all information about the constructions of various multivariate wavelet estimates. The command is.mvLSW checks that the supplied R object is a valid mvLSW object in that its structure and contents are as expected.

Examples

Run this code

## Define evolutionary wavelet spectrum, structure only on level 2
Spec <- array(0, dim = c(3, 3, 8, 256))
Spec[1, 1, 2, ] <- 10
Spec[2, 2, 2, ] <- c(rep(5, 64), rep(0.6, 64), rep(5, 128))
Spec[3, 3, 2, ] <- c(rep(2, 128), rep(8, 128))
Spec[2, 1, 2, ] <- Spec[1, 2, 2, ] <- c(rep(0, 64), seq(0, 1, len = 128), rep(1, 64))
Spec[3, 1, 2, ] <- Spec[1, 3, 2, ] <- c(rep(-1, 128), rep(5, 128))
Spec[3, 2, 2, ] <- Spec[2, 3, 2, ] <- -0.5

## Define EWS as mvLSW object
EWS <- mvLSW(spectrum = Spec, filter.number = 1, family = "DaubExPhase", 
  names = c("A", "B", "C"), min.eig.val = NA)
is.mvLSW(EWS)
plot(EWS, style = 2, info = 2)