# SDF for various stochastic fractal time series models

### Description

Compute a discretized version of a single-sided parametric spectral density function (SDF) for various stochastic fractal time series models.

### Usage

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### Arguments

`x` |
an object of class |

`n.freq` |
the number of frequencies at which the SDF is computed
(this argument should not be supplied if |

`n.sample` |
length of a time series.
If non-NULL, the spectral resolution is set to |

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

`with.Nyquist` |
a logical flag. If |

### Details

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).

### Value

an object of class `signalSeries`

containing the SDF.

### References

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.

### See Also

`lmModel`

, `lmACF`

, `lmSimulate`

.

### Examples

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