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Last data update: 2014.03.03

R: Density Functions of Mixture Models

ddmix

R Documentation

Density Functions of Mixture Models

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.

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(EMMIXskew)
Loading required package: lattice
Loading required package: mvtnorm
Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/EMMIXskew/ddmix.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ddmix
> ### Title: Density Functions of Mixture Models
> ### Aliases: ddmix
> ### Keywords: cluster
> 
> ### ** 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)
          [,1]      [,2]      [,3]
[1,] -10.49563 -8.278297 -5.645026
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>