Description Usage Arguments Details Value See Also Examples

emHMMR implements the maximum-likelihood parameter estimation of the HMMR 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` |
Numeric vector of length |

`K` |
The number of regimes/segments (HMMR components). |

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

`variance_type` |
Optional character indicating if the model is "homoskedastic" or "heteroskedastic" (i.e same variance or different variances for each of the K regmies). 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. |

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

of the class ParamHMMR, then it alternates between
the E-Step (method of the class StatHMMR) and the M-Step
(method of the class ParamHMMR) 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 ModelHMMR.

ModelHMMR, ParamHMMR, StatHMMR

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

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.