mgc: Mixture Gaussian Clustering

In PERMANOVA: Multivariate Analysis of Variance Based on Distances and Permutations

Description

Model based clustering using mixtures of gaussian distributions.

Usage

mgc(x, NG = 2, init = "km", RemoveOutliers = FALSE, ConfidOutliers = 0.995,
tolerance = 1e-07, maxiter = 100, show = TRUE, ...)

Arguments

x	The data matrix.
NG	Number of groups or clusters to obtain.
init	Initial centers can be obtained from k-means ("km") or at random ("rd").
RemoveOutliers	Should the extreme values be removed to calculate the clusters?
ConfidOutliers	Percentage of the points to keep for the calculations when RemoveOutliers is true.
tolerance	Tolerance for convergence.
maxiter	Maximum number of iterations.
show	Should the likelihood at each iteration be shown?
...	Any other parameter that can affect k-means if that is the initial configuration.

Details

A basic algorithm for clustering with mixtures of gaussians with no restrictions on the covariance matrices.

Value

Clusters.

Author(s)

Jose Luis Vicente-Villardon

Examples

X=as.matrix(iris[,1:4])
mod1=mgc(X,NG=3)
plot(iris[,1:4], col=mod1$Classification)
table(iris[,5],mod1$Classification)

PERMANOVA documentation built on Sept. 6, 2021, 5:07 p.m.