NB.MClust: NB.MClust Function
In NB.MClust: Negative Binomial Model-Based Clustering

Description Usage Arguments Value Examples

This function performs model-based clustering on positive integer or continuous data that follow Generalized Negative Binomial distribution.

1 2	NB.MClust(Count, K, ini.shift.mu = 0.01, ini.shift.theta = 0.01, tau0 = 10, rate = 0.9, bic = TRUE, iteration = 100)

`Count`	Data matrix of discrete counts.This function groups rows of the data matrix.
`K`	Number of clusters or components specified. It can be a positive integer or a vector of positive integer.
`ini.shift.mu`	Initial value in EM algorithm for the shift between clusters in mean.
`ini.shift.theta`	Initial value in EM algorithm for the shift between clusters in dispersion.
`tau0`	Initial value of anealing rates in EM Algorithm. Default and suggested value is 10.
`rate`	Stochastic decreasing speed for anealing rate. Default and suggested value is 0.9
`bic`	Whether Bayesian Information should be computed when K is an integer. BIC is forced to be TRUE when K is a vector.
`iteration`	Maximum number of iterations in EM Algorithm, default at 50.

`parameters`	Estimated parameters
`$prior`	Prior probability that a sample belongs to each cluster
`$mu`	Mean of each cluster
`$theta`	Dispersion of each cluster
`$posterior`	Posterior probability that a sample belongs to each cluster
`cluster`	Estimated cluster assignment
`BIC`	Value of Bayesian Information
`K`	Optional or estimated number of clusters, if input K is a vector

# Example:

data("Simulated_Count") # A 50x100 integer data frame.

m1=NB.MClust(Simulated_Count,K=2:5)
cluster=m1$cluster #Estimated cluster assignment
k_hat=m1$K  #Estimated optimal K