Description Usage Arguments Details Value See Also Examples

cemMixRHLP implements the maximum complete likelihood parameter estimation of mixture of RHLP models by the Classification Expectation-Maximization algorithm (CEM algorithm).

1 2 3 4 |

`X` |
Numeric vector of length |

`Y` |
Matrix of size |

`K` |
The number of clusters (Number of RHLP models). |

`R` |
The number of regimes (RHLP components) for each cluster. |

`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". |

`init_kmeans` |
Optional. A logical indicating whether or not the curve partition should be initialized by the K-means algorithm. Otherwise the curve partition is initialized randomly. |

`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. |

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

of the class ParamMixRHLP, then it alternates
between the E-Step, the C-Step (methods of the class
StatMixRHLP), and the CM-Step (method of the class
ParamMixRHLP) 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 ModelMixRHLP.

ModelMixRHLP, ParamMixRHLP, StatMixRHLP

1 2 3 4 5 6 7 8 9 10 11 12 | ```
data(toydataset)
#' # Let's fit a mixRHLP model on a dataset containing 2 clusters:
data <- toydataset[1:190,1:21]
x <- data$x
Y <- t(data[,2:ncol(data)])
mixrhlp <- cemMixRHLP(X = x, Y = Y, K = 2, R = 2, p = 1, verbose = TRUE)
mixrhlp$summary()
mixrhlp$plot()
``` |

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