Description Usage Arguments Details Value Author(s) References See Also Examples

Generate a random time series of the 1-dimensional stationary Ornstein-Uhlenbeck state space (OUSS) process.

1 2 | ```
generate_ouss(times, mu, power_o, sigma,
lambda, power_e, epsilon)
``` |

`times` |
Numeric vector of times for which to evaluate OUSS model. Times need to be strictly increasing. |

`mu` |
Single number. Deterministic equilibrium of OU process, i.e., the expected value of the time series at any particular time. |

`sigma` |
Single number. Standard deviation of OU fluctuations around equilibrium. |

`power_o` |
Single non-negative number. Power spectrum at zero-frequency generated by the OU process. Either |

`lambda` |
Single non-negative number. Resilience (also known as relaxation rate) of the OU process. This is the inverse of the OU correlation time. |

`epsilon` |
Single number. Standard deviation of Gaussian measurement error. Setting this to zero will yield a time series from the classical OU process. |

`power_e` |
Single non-negative number. Asymptotic power spectrum at large frequencies due to the Gaussian measurement errors. Setting this to zero will yield a classical OU process. Either |

The OUSS model describes the measurement of an Ornstein-Uhlenbeck (OU) stochastic process at discrete times with additional uncorrelated Gaussian measurement errors. The OU process itself is a continuous-time random walk (Brownian motion) with linear stabilizing forces, described by the stochastic differential equation

*
dX = λ(μ-X) dt + s dW,
*

where *W* is the standard Wiener process and *s^2=2λσ^2.* The OUSS model is obtained by adding uncorrelated Gaussian numbers with zero mean and variance *ε^2* to the time series.

A numeric vector of same length as `times`

, containing sampled values of the OUSS process. These values will all have the same expectation (`mu`

) and variance (`sigma^2+epsilon^2`

) but will be correlated.

Stilianos Louca

Louca, S., Doebeli, M. (2015) Detecting cyclicity in ecological time series, Ecology 96: 1724–1732

Dennis, B., Ponciano, J.M. - Density dependent state-space model for population abundance data with unequal time intervals, Ecology (in press as of June 2014)

1 2 3 4 5 6 7 8 9 10 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.