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)