model_selection | R Documentation |

Compares different models and return the best one selected according to criterion (BIC or AIC).

model_selection(y, layers, g, seeds = sample(.Machine$integer.max, 10), it = 50, eps = 0.001, init = "kmeans", init_est = "factanal", criterion = "BIC")

`y` |
A matrix or a data frame in which rows correspond to observations and columns to variables. |

`layers` |
The number of layers in the deep Gaussian mixture model. Admitted values are 1, 2 or 3. |

`g` |
The number of clusters. |

`seeds` |
Integer vector containing seeds to try. |

`it` |
Maximum number of EM iterations. |

`eps` |
The EM algorithm terminates the relative increment of the log-likelihod falls below this value. |

`init` |
Initial paritioning of the observations to determine initial parameter values. See Details. |

`init_est` |
To determine how the initial parameter values are computed. See Details. |

`criterion` |
Model selection criterion, either |

Compares different models and return the best one selected according to criterion (BIC or AIC). One can use diffefrent number of seeds.

A list containing
an object of class `"dgmm"`

containing fitted values
and list of BIC and AIC values.

Viroli, C. and McLachlan, G.J. (2019). Deep Gaussian mixture models. Statistics and Computing 29, 43-51.

y <- scale(mtcars) sel <- model_selection(y, layers = 2, g = 3, seeds = c(1, 2, 12334), it = 250, eps = 0.001, init = "kmeans", criterion = "BIC") sel summary(sel)

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