lmSimulate {fractal} | R Documentation |
Create a simulation of a stochastic fractal time series according to a specfied model.
lmSimulate(x, sampling.interval=1, mean=0, n.sample=128, generate.Sj=FALSE, Sj=NULL, rn=NULL)
x |
an object of class "lmModel" . Use the lmModel function
to create this input. |
Sj |
a numeric vector of Davies-Harte frequency
domain weights used to create the simulation(s). These weights
are calculated if not supplied. Default: NULL (not supplied). |
generate.Sj |
a logical value. If TRUE , the Davies-Harte frequency
domain weights (Sj) are returned instead of a simulated series. See the references
for details. Default: FALSE . |
mean |
the mean value of of the resulting simulation. Default: 0.0 . |
n.sample |
length of a time series.
Default: 128 . |
rn |
a vector of random normal deviates used to generate
uncorrelated random variables for the Davies-Harte simulator.
Default: rnorm(2 * length(Sj) - 2) . |
sampling.interval |
the sampling interval for the process.
The SDF is computed for frequencies on the interval [0, Nyquist]
where Nyquist is 1/(2*sampling.interval) .
The value of sampling.interval must be a positive number. Default: 1 . |
Simulates a stochastic fractal time series via the Davies-Harte technique, which randomizes spectral weights and inverts the result back to the time domain. See the references for more details.
an object of class signalSeries
containing the simulated series.
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.
Davies,R.B.and Harte,D.S.(1987). Tests for the Hurst effect, Biometrika, 74, 95–102.
lmModel
, lmACF
, lmSDF
, lmConfidence
, FDSimulate
.
old.plt <- par("plt") models <- c("ppl","fdp","fgn","dfbm") for (i in seq(along=models)){ splitplot(2,2,i) plot(lmSimulate(lmModel(models[i])), reference.grid=FALSE) } par(plt=old.plt)