| wavFDP {wmtsa} | R Documentation |
Class constructor for block- and time-dependent wavelet-based FD model parameter estimators.
wavFDP(estimator, delta, variance.delta,
innovations.variance, delta.range, dictionary, levels,
edof.mode, boundary, series, sdf.method, type)
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 description should contain
a description of the boundary treatment. |
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". |
NULL (no reference line)."Time".NULL (no title).par function. Default: "l" (solid line).NULL (no reference line)."Time".NULL (no title).par function. Default: "l" (solid line).TRUE, a key of the plot is shown. Default: TRUE.par for the confidence intervals. Default: 16.5.
## 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)