Description Usage Arguments Details Value See Also Examples

selecRHLP implements a model selection procedure to select an optimal RHLP model with unknown structure.

1 2 | ```
selectRHLP(X, Y, Kmin = 1, Kmax = 10, pmin = 0, pmax = 4,
criterion = c("BIC", "AIC"), verbose = TRUE)
``` |

`X` |
Numeric vector of length |

`Y` |
Numeric vector of length |

`Kmin` |
The minimum number of regimes (RHLP components). |

`Kmax` |
The maximum number of regimes (RHLP components). |

`pmin` |
The minimum order of the polynomial regression. |

`pmax` |
The maximum order of the polynomial regression. |

`criterion` |
The criterion used to select the RHLP model ("BIC", "AIC"). |

`verbose` |
Optional. A logical value indicating whether or not a summary of the selected model should be displayed. |

selectRHLP selects the optimal MRHLP model among a set of model
candidates by optimizing a model selection criteria, including the Bayesian
Information Criterion (BIC). This function first fits the different RHLP
model candidates by varying the number of regimes `K`

from `Kmin`

to `Kmax`

and the order of the polynomial regression `p`

from `pmin`

to `pmax`

. The
model having the highest value of the chosen selection criterion is then
selected.

selectRHLP returns an object of class ModelRHLP
representing the selected RHLP model according to the chosen `criterion`

.

ModelRHLP

1 2 3 4 5 6 7 8 9 | ```
data(univtoydataset)
# Let's select a RHLP model on a time series with 3 regimes:
data <- univtoydataset[1:320,]
selectedrhlp <- selectRHLP(X = data$x, Y = data$y,
Kmin = 2, Kmax = 4, pmin = 0, pmax = 1)
selectedrhlp$summary()
``` |

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