Description Usage Arguments Details Value References See Also Examples

A wrapper function around `ll.order`

to calculate prediction profiles
(a la Sugihara and May 1990 and Yao and Tong 1994). The method uses
leave-one-out cross-validation of the local regression (with CV optimized
bandwidth) against lagged-abundances at various lags.

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`x` |
A time series without missing values. |

`step` |
The vector of time steps for forward prediction. |

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

`deg` |
The degree of the local polynomial. |

`bandwidth` |
The candidate bandwidths to be considered. |

see `ll.order`

for details.

An object of class "ppll" consisting of a list with the following components:

`step` |
the prediction steps considered. |

`CV` |
the cross-validation error. |

`order` |
the optimal order for each step. |

`bandwidth` |
the optimal bandwidth for each step. |

`df` |
the degrees of freedom for each step. |

Sugihara, G., and May, R.M. (1990) Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 344, 734-741. https://doi.org/10.1038/344734a0

Yao, Q. and Tong, H. (1994) Quantifying the influence of initial values on non-linear prediction. Journal of Royal Statistical Society B, 56, 701-725.

Fan, J., Yao, Q., and Tong, H. (1996) Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems. Biometrika, 83, 189-206. https://doi.org/10.1093/biomet/83.1.189

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