Description Usage Arguments Details Value See Also Examples

emRHLP implements the maximum-likelihood parameter estimation of the RHLP model by the Expectation-Maximization (EM) algorithm.

1 2 3 |

`X` |
Numeric vector of length |

`Y` |
Numeric vector of length |

`K` |
The number of regimes (RHLP components). |

`p` |
Optional. The order of the polynomial regression. By default, |

`q` |
Optional. The dimension of the logistic regression. For the purpose of segmentation, it must be set to 1 (which is the default value). |

`variance_type` |
Optional character indicating if the model is "homoskedastic" or "heteroskedastic". By default the model is "heteroskedastic". |

`n_tries` |
Optional. Number of runs of the EM algorithm. The solution providing the highest log-likelihood will be returned. If |

`max_iter` |
Optional. The maximum number of iterations for the EM algorithm. |

`threshold` |
Optional. A numeric value specifying the threshold for the relative difference of log-likelihood between two steps of the EM as stopping criteria. |

`verbose` |
Optional. A logical value indicating whether or not values of the log-likelihood should be printed during EM iterations. |

`verbose_IRLS` |
Optional. A logical value indicating whether or not values of the criterion optimized by IRLS should be printed at each step of the EM algorithm. |

emRHLP function implements the EM algorithm for the RHLP model. This
function starts with an initialization of the parameters done by the method
`initParam`

of the class ParamRHLP, then it alternates between
the E-Step (method of the class StatRHLP) and the M-Step
(method of the class ParamRHLP) until convergence (until the
relative variation of log-likelihood between two steps of the EM algorithm
is less than the `threshold`

parameter).

EM returns an object of class ModelRHLP.

ModelRHLP, ParamRHLP, StatRHLP

1 2 3 4 5 6 7 | ```
data(univtoydataset)
rhlp <- emRHLP(univtoydataset$x, univtoydataset$y, K = 3, p = 1, verbose = TRUE)
rhlp$summary()
rhlp$plot()
``` |

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