Description Usage Arguments Details Value See Also Examples

emMHMMR implements the maximum-likelihood parameter estimation of the MHMMR model by the Expectation-Maximization (EM) algorithm, known as Baum-Welch algorithm in the context of HMMs.

1 2 3 |

`X` |
Numeric vector of length |

`Y` |
Matrix of size |

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

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

`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. |

emMHMMR function implements the EM algorithm. This function starts
with an initialization of the parameters done by the method `initParam`

of
the class ParamMHMMR, then it alternates between the E-Step
(method of the class StatMHMMR) and the M-Step (method of the
class ParamMHMMR) 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 ModelMHMMR.

ModelMHMMR, ParamMHMMR, StatMHMMR

1 2 3 4 5 6 7 8 | ```
data(multivtoydataset)
mhmmr <- emMHMMR(multivtoydataset$x, multivtoydataset[,c("y1", "y2", "y3")],
K = 5, p = 1, verbose = TRUE)
mhmmr$summary()
mhmmr$plot()
``` |

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