View source: R/bic.alfamixnorm.R

Mixture model selection with the alpha-transformation using BIC | R Documentation |

Mixture model selection with the *α*-transformation using BIC.

bic.alfamixnorm(x, G, a = seq(-1, 1, by = 0.1), 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. |

`a` |
A vector with a grid of values of the power transformation, it has to be between -1 and 1. If zero values are present
it has to be greater than 0. If |

`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 *α*-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 list including:

`abic` |
A list that contains the matrices of all BIC values for all values of |

`optalpha` |
The value of |

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

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

If graph is set equal to TRUE a plot with the BIC of the best model for each number of components versus the number of components and a list with the results of the Gaussian mixture model for each value of *α*.

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.

Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf

```
alfa.mix.norm, mix.compnorm, mix.compnorm.contour, rmixcomp, alfa, alfa.knn,
alfa.rda, comp.nb
```

x <- as.matrix( iris[, 1:4] ) x <- x/ rowSums(x) bic.alfamixnorm(x, 1:3, a = c(0.4, 0.5, 0.6), graph = FALSE)

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.