Description Usage Arguments Details Value See Also Examples

emSNMoE implements the maximum-likelihood parameter estimation of a Skew-Normal Mixture of Experts (SNMoE) model by the Expectation Conditional Maximization (ECM) algorithm.

1 2 |

`X` |
Numeric vector of length |

`Y` |
Numeric vector of length |

`K` |
The number of experts. |

`p` |
Optional. The order of the polynomial regression for the experts. |

`q` |
Optional. The order of the logistic regression for the gating network. |

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

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

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

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

emSNMoE function implements the ECM algorithm for the SNMoE model.
This function starts with an initialization of the parameters done by the
method `initParam`

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

parameter).

ECM returns an object of class ModelSNMoE.

ModelSNMoE, ParamSNMoE, StatSNMoE

1 2 3 4 5 6 7 8 9 | ```
data(tempanomalies)
x <- tempanomalies$Year
y <- tempanomalies$AnnualAnomaly
snmoe <- emSNMoE(X = x, Y = y, K = 2, p = 1, verbose = TRUE)
snmoe$summary()
snmoe$plot()
``` |

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