hypvol: Aproximate Hypervolume for Multivariate Data

In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

Aproximate Hypervolume for Multivariate Data

Description

Computes a simple approximation to the hypervolume of a multivariate data set.

Usage

hypvol(data, reciprocal=FALSE)

Arguments

data	A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
reciprocal	A logical variable indicating whether or not the reciprocal hypervolume is desired rather than the hypervolume itself. The default is to return the hypervolume.

Value

Returns the minimum of the hypervolume computed from simple variable bounds and that computed from variable bounds of the principal component scores. Used for the default hypervolume parameter for the noise component when observations are designated as noise in Mclust and mclustBIC.

References

A. Dasgupta and A. E. Raftery (1998). Detecting features in spatial point processes with clutter via model-based clustering. Journal of the American Statistical Association 93:294-302.

C. Fraley and A.E. Raftery (1998). Computer Journal 41:578-588.

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.

See Also

mclustBIC

Examples

hypvol(iris[,-5])

mclust documentation built on Nov. 16, 2023, 5:10 p.m.