Description Usage Arguments Value
Run EM for a multinomial mixture model on a sample x feature matrix.
Take a matrix counts
and classify the samples in each row into
one of k
clusters. The initial parameters of the multinomial
mixture model must be given as a list in the parameter mn_params
.
1 2 
counts 
observation by variable matrix of nonnegative integer counts. 
k 
Number of clusters. 
mn_params 
A list containing

max_iter 
A numeric value indivating the maximum number of iterations. 
eps 
The epsilon value, which is the convergence threshold of the percent change in the log likelihood. 
psc 
Pseudocount to add to the multinomial mean parameter to avoid the likelihood collapsing to 0. 
labels 
Numeric vector of same length as number of observations in counts. Fixes the group probabilities of the integer in this vector element to 1. In other words, the latent variable for these samples are treated as known. 
verbose 
verbosity. 
A list with
An observation by cluster matrix of log log likelihoods. Each element is the log likelihood of that data point under the the k multinomial.
A variable by cluster matrix of multinomial parameters.
A numeric vector of mixture coefficients.
Data log likelihood.
logical indicating whether the EM algorithm converged (TRUE) or reached the maximum number of iterations.
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