gaussmixt.init: Provides initial values for the parameters of a mixture of...

In condmixt: Conditional Density Estimation with Neural Network Conditional Mixtures

Description

Initial values for the parameters of a mixture of Gaussians are provided by applying the following steps : 1) clustering the sample into as many clusters as there are mixture components 2) the initial means and standard deviations of each component are taken as the cluster centers and median absolute deviation respectively computed on each component

Usage

gaussmixt.init(m, x, iter.max = 20, nstart = 10)

Arguments

m	number of mixture components
x	data sample from which the initial parameters are computed
iter.max	maximum number of iteration for kmeans clustering, default is 20, see kmeans
nstart	number of random cluster centers chosen (default is 10), see kmeans

Value

a matrix of dimension 3 x m which stores the 3 parameters (pi, mu, sigma) of each of the m components.

Author(s)

Julie Carreau

References

McLachlan, G. and Peel, D. (2000), Finite Mixture Models, Wiley series in probability and statistics

See Also

kmeans,hparetomixt.init

Examples

r <- rfrechet(500,loc=5,scale=5,shape=10)
m <- 2
param.init <- gaussmixt.init(2,r)

condmixt documentation built on July 1, 2020, 6:04 p.m.