Mixture model selection via BIC {Compositional} | 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. |
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. |
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, mixnorm.contour, rmixcomp, bic.alfamixnorm
## Not run: 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) ## End(Not run)