# getICL: The ICL criterion In EMMIXskew: The EM Algorithm and Skew Mixture Distribution

## Description

Calculate the Integrated Completed Likelihood(ICL) criterion

## Usage

 `1` ```getICL(x, n, p, g, distr, ncov, pro, mu, sigma, dof, delta, clust) ```

## Arguments

 `x` An n by p data matrix `n` The total number of points `p` Dimension of data `g` the number of components of the mixture model `distr` A three letter string indicating the type of distribution to be fit. `ncov` A small integer indicating the type of covariance structure. `pro` A vector of mixing proportions `mu` A numeric matrix with each column corresponding to the mean `sigma` An array of dimension (p,p,g) with first two dimension corresponding covariance matrix of each component `dof` A vector of degrees of freedom for each component `delta` A p by g matrix with each column corresponding to a skew parameter vector `clust` A vector of partition

## Value

 `ICL` ICL value

## References

Biernacki C. Celeux G., and Govaert G. (2000). Assessing a Mixture Model for Clustering with the integrated Completed Likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence. 22(7). 719-725.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```n1=300;n2=300;n3=400; nn <-c(n1,n2,n3) n=1000 p=2 ng=3 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)) pro <- c(0.3,0.3,0.4) distr="mvn" ncov=3 #first we generate a data set set.seed(111) #random seed is set dat <- rdemmix(nn,p,ng,distr,mu,sigma,dof=NULL,delta=NULL) #start from initial partition clust<- rep(1:ng,nn) obj <- EmSkewfit1(dat, ng, clust, distr, ncov, itmax=1000,epsilon=1e-4) getICL(dat,n,p,ng, distr,ncov,obj\$pro,obj\$mu,obj\$sigma,obj\$dof, obj\$delta,obj\$clust) ```

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