wmtsa (version 1.1-1)

wavFDP: Class constructor for block- and time-dependent wavelet-based FD model parameter estimators

Description

Class constructor for block- and time-dependent wavelet-based FD model parameter estimators.

Usage

wavFDP(estimator, delta, variance.delta,
    innovations.variance, delta.range, dictionary, levels,
    edof.mode, boundary, series, sdf.method, type)

Arguments

estimator

character string briefly describing the estimator.

delta

numeric value/vector denoting the estimated FD model parameter.

innovations.variance

numeric value/vector denoting the estimated FD innovations variance.

variance.delta

numeric value/vector defining the variance of delta.

delta.range

two element numeric vector defining the range of delta.

dictionary

wavelet transform dictionary used in the analysis.

levels

vector of integers denoting the wavelet decomposition levels used in the analysis.

edof.mode

an integer on [1,3] defining the equivalent degrees of freedom mode used in the analysis.

boundary

a list containing named objects mode and description, containing a logical value and a character string, respectively. The mode object should be be TRUE if a boundary treatment was used, and descri

series

a signSeries object containing the input series.

sdf.method

a character string defining the SDF method used in the analysis, e.g., "Integration lookup table".

type

a character string defining the type of estimator, e.g., ""instantaneous"" or "block".

concept

class constructor

See Also

wavFDPBlock, wavFDPTime.

Examples

## create a faux dictionary 
dictionary <- wavDictionary(wavelet="s8",
    dual=FALSE, decimate=FALSE, n.sample=512,
    attr.x=NULL, n.levels=5,
    boundary="periodic", conv=TRUE,
    filters=wavDaubechies("s8"),
    fast=TRUE, is.complex=FALSE)

## construct a faux wavFDP object 
z <- wavFDP(estimator="wlse",
    delta=0.45,
    variance.delta=1.0,
    innovations.variance=1.0,
    delta.range=c(-10.0,10.0),
    dictionary=dictionary,
    levels=c(1,3:4),
    edof.mode=2,
    boundary=list(mode=TRUE,description="unbiased"),
    series=create.signalSeries(fdp045),
    sdf.method="Integration lookup table",
    type="block")

## print the result 
print(z)