lmSDF: SDF for various stochastic fractal time series models
Compute a discretized version of a single-sided parametric spectral density function (SDF)
for various stochastic fractal time series models.
(x, sampling.interval=1, n.freq=,
an object of class
"lmModel". Use the
to create this input.
the number of frequencies at which the SDF is computed
(this argument should not be supplied if
n.sample is supplied).
n.sample is non-NULL supplied but
n.freq is NULL,
the actual grid of frequencies is determined by the argument
with.Nyquist. Default: if neither
n.freq is specified,
n.freq defaults to 32.
length of a time series.
If non-NULL, the spectral resolution is set to
1/(n.sample * sampling.interval).
n.freq is used to set the specral resolution instead).
the sampling interval for the process.
The SDF is computed for frequencies on the interval [0, Nyquist]
where Nyquist is
The value of
sampling.interval must be a positive number. Default:
a logical flag. If
the grid of frequencies over which the SDF is evaluated
ranges from 1/2*n.freq*sampling.interval up to the Nyquist frequency;
the range is from 1/(2*n.freq + 1)*sampling.interval to just below
the Nyquist frequency.
The intent of this argument is to mimic the grid
of Fourier frequencies for time series with
an even or odd sample size
by setting with.Nyquist
This argument is only really intended to be used
n.sample is not supplied, but
n.freq is. Default:
The SDF is computed as described in Section 7.6 of Percival and Walden (2000),
after a possible change of variable to take into account
the sampling interval (the discussion in the reference assumes a unit sampling interval).
an object of class
signalSeries containing the SDF.
D. Percival and A. Walden (2000),
Wavelet Methods for Time Series Analysis,
Cambridge University Press, Chapter 7.
J. Beran (1994),
Statistics for Long-Memory Processes,
Chapman and Hall, Chapter 2.
D. Percival and A. Walden (1993),
Spectral Analysis for Physical Applications,
Cambridge University Press, 1993, Chapter 9.
old.plt <- ("plt")
models <- ("ppl","fdp","fgn","dfbm")