ddmix: Density Functions of Mixture Models

In EMMIXskew: The EM Algorithm and Skew Mixture Distribution

Description

Calculate the density of multivariate mixture models at data points for each component

Usage

ddmix(  dat, n, p, g, distr, mu, sigma, dof=NULL, delta=NULL)

Arguments

dat	The dataset
n	The total number of points
p	Dimension of data
g	The number of clusters
distr	A three letter string indicating the distribution; "mvn" for normal, "mvt" for t distribution, "msn" for skew normal, and "mst" for skew t distribution.
mu	A numeric mean matrix with each column corresponding to the mean
sigma	An array of dimension (p,p,g) with first two dimensions corresponding covariance matrix of each component
dof	A vector of degrees of freedom for each component
delta	A matrix with each column as skew parameter vector

Value

ddmix gives an n by g matrix of logorithm of density at each data point for each component.

References

McLachlan G.J. and Krishnan T. (2008). The EM Algorithm and Extensions (2nd). New Jersay: Wiley.

McLachlan G.J. and Peel D. (2000). Finite Mixture Models. New York: Wiley.

See Also

ddmvn,ddmvt,ddmsn,ddmst.

Examples

p=2
g=3

#mixing propotion of each component
pro   <- c(0.3,0.3,0.4)

#specify mean and covariance matrix for each component

sigma<-array(0,c(2,2,3))
for(h in 2:3) sigma[,,h]<-diag(2)
sigma[,,1]<-cbind( c(1,0),c(0,.1))

mu  <- cbind(c(4,-4),c(3.5,4),c( 0, 0))

#specify other parameters for "mvt","msn","mst"

delta <- cbind(c(3,3),c(1,5),c(-3,1))
dof    <- c(3,5,5)

#specify the distribution
distr <- "mst"

y <- c(1,2)

n=1

#then the density value at y for the mixture model is 

ddmix(y, n, p, g, distr, mu, sigma, dof, delta)

EMMIXskew documentation built on May 2, 2019, 11:07 a.m.