mxnormal {lazy.cluster} R Documentation

## Fitting Finite Mixture of Normal Distributions

### Description

Fitting Finite Mixture of Normal Distributions

### Usage

```mxnormal(X, ncl, rho, mu, Sigma, maxiter = 100, eps = 1e-05, print = 0,
debug = 0)
```

### Arguments

 `X` Input data matrix: nobs x nvar `ncl` # of components to be mixed `rho` vector of mixing proportions `mu` ncl x nvar matrix of means `Sigma` nvar x nvar x ncl array of Dispersion matrices `maxiter` max # of iterations `eps` criterion for the relative improvement of lmlh `print` = 1 to print the result `debug` = 1 to print the intermediate result

### Details

This function uses EM algorithm to estimate the parameters of a finite mixture of ncl normal distribtuions.
The parameters, namely, rho, mu and Sigma, must have the initial values.

### Value

A list of
rho: vector of mixing proportions
mu: ncl x nvar matrix of means
Sigma: nvar x nvar x ncl array of Dispersion matrices
lmlh: log marginal likelihood maximized
cluster: classification vector of observations
H: posterior probability matrix

### Examples

```# initial by kmeans
ncl=2
# data=as.matrix(circle2[,1,drop=0])
data=circle2
resk=kmeans( data, ncl )
rho=resk\$size/sum(resk\$size)
mu=resk\$centers
var=resk\$tot.withinss/sum(resk\$size)
Disp=var*diag(ncol(mu))
Sigma=t(repmat(t(Disp),ncl))
dim(Sigma)=c(ncol(mu),ncol(mu),ncl)
resm=mxnormal( data, ncl, rho, mu, Sigma, print=1 )

```

[Package lazy.cluster version 0.1.2 Index]