mgc {PERMANOVA}

Mixture Gaussian Clustering

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)