Description Usage Arguments Value References Examples
Calculate the Integrated Completed Likelihood(ICL) criterion
1 |
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 |
ICL |
ICL value |
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.
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)
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