View source: R/bic.mixcompnorm.R

Mixture model selection via BIC | R Documentation |

Mixture model selection via BIC.

bic.mixcompnorm(x, G, type = "alr", veo = FALSE, graph = TRUE)

`x` |
A matrix with compositional data. |

`G` |
A numeric vector with the number of components, clusters, to be considered, e.g. 1:3. |

`type` |
The type of trasformation to be used, either the additive log-ratio ("alr"), the isometric log-ratio ("ilr") or the pivot coordinate ("pivot") transformation. |

`veo` |
Stands for "Variables exceed observations". If TRUE then if the number variablesin the model exceeds the number of observations, but the model is still fitted. |

`graph` |
A boolean variable, TRUE or FALSE specifying whether a graph should be drawn or not. |

The alr or the ilr-transformation is applied to the compositional data first and then mixtures of multivariate Gaussian distributions are fitted. BIC is used to decide on the optimal model and number of components.

A plot with the BIC of the best model for each number of components versus the number of components. A list including:

`mod` |
A message informing the user about the best model. |

`BIC` |
The BIC values for every possible model and number of components. |

`optG` |
The number of components with the highest BIC. |

`optmodel` |
The type of model corresponding to the highest BIC. |

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

Ryan P. Browne, Aisha ElSherbiny and Paul D. McNicholas (2018). mixture: Mixture Models for Clustering and Classification. R package version 1.5.

Ryan P. Browne and Paul D. McNicholas (2014). Estimating Common Principal Components in High Dimensions. Advances in Data Analysis and Classification, 8(2), 217-226.

Aitchison J. (1986). The statistical analysis of compositional data. Chapman & Hall.

```
mix.compnorm, mix.compnorm.contour, rmixcomp, bic.alfamixnorm
```

x <- as.matrix( iris[, 1:4] ) x <- x/ rowSums(x) bic.mixcompnorm(x, 1:3, type = "alr", graph = FALSE) bic.mixcompnorm(x, 1:3, type = "ilr", graph = FALSE)

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