Description Usage Arguments Details Value References See Also Examples

A function to estimate the order of a time series using cross-validation of the linear autoregressive model. Coefficients are estimated using conditional least-squares. I coded this functions to estimate the order of ecological time series. Bjornstad et al. (1998, 2001)

1 | ```
lin.order.cls(x, order = 1:5, n.cond = 5, echo = TRUE)
``` |

`x` |
A time series without missing values |

`order` |
The candidate orders. The default is 1:5 |

`n.cond` |
The number of observation to condition on. The default is 5 (must be >= max(order)) |

`echo` |
if TRUE a counter for the data points and the orders is produced to monitor progress. |

The time series is normalized prior to cross-validation.

Note that if the dynamics is highly nonlinear, the nonparametric
order-estimators (`ll.order`

) may be more appropriate. (I coded
this function to use for comparison with the nonparametric methods, because
these also uses (nonlinear) conditional least-squares.)

An object of class "lin.order" is returned consisting of the following components:

`order` |
the grid of orders considered. |

`CVd` |
the cross-validation errors across the grid of orders. |

Bjornstad, O.N., Begon, M., Stenseth, N. C., Falck, W., Sait, S. M. and Thompson, D. J. 1998. Population dynamics of the Indian meal moth: demographic stochasticity and delayed regulatory mechanisms. Journal of Animal Ecology 67:110-126. https://doi.org/10.1046/j.1365-2656.1998.00168.x Bjornstad, O.N., Sait, S.M., Stenseth, N.C., Thompson, D.J. & Begon, M. 2001. Coupling and the impact of specialised enemies on the dimensionality of prey dynamics. Nature 401: 1001-1006. https://doi.org/10.1038/35059003

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