Create a simulation of a stochastic fractal time series according to a specfied model.

1 2 3 | ```
lmSimulate(x, sampling.interval=1,
mean=0, n.sample=128, generate.Sj=FALSE,
Sj=NULL, rn=NULL)
``` |

`x` |
an object of class |

`Sj` |
a numeric vector of Davies-Harte frequency
domain weights used to create the simulation(s). These weights
are calculated if not supplied. Default: |

`generate.Sj` |
a logical value. If |

`mean` |
the mean value of of the resulting simulation. Default: |

`n.sample` |
length of a time series.
Default: |

`rn` |
a vector of random normal deviates used to generate
uncorrelated random variables for the Davies-Harte simulator.
Default: |

`sampling.interval` |
the sampling interval for the process.
The SDF is computed for frequencies on the interval [0, Nyquist]
where Nyquist is |

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`

.

1 2 3 4 5 6 7 8 |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.