EM.HODC: Hierachical ordered density clustering (HODC) Algorithm with...
In BANFF: Bayesian Network Feature Finder

Description Usage Arguments Details Value Examples

Hierachical ordered density clustering (HODC) Algorithm with input generated by Mclust

1	EM.HODC(pvalue)

pvalue

a vector of p-values obtained from large scale statistical hypothesis testing

Without the information of networking, we can have an approximation to the marginal density by DPM model fitting on r. Suppose the number of finite mixture normals is equal to L_0+L_1, which means the number of classes we have, we apply HODC algorithm in partitioning the $L_0$ and $L_1$ components into two classes, For this function, the input is generated by Mclust

a list of HODC algorithm returned parameters.

mean: the mean of each of two clusters
variance: the variance of each of two clusters
pro: the probability of each of two clusters
classificaiton: The classification corresponding to each cluster

## Not run: 
rstat=c(rnorm(50,mean=1),rnorm(50,mean=2),rnorm(100,mean=4),rnorm(100,mean=8))
pvalue=pnorm(-rstat)
mclustHODC=EM.HODC(pvalue)

## End(Not run)