vmm_fit_em_by_ll: Estimate von Mises Mixture parameters using Expectation...

In merkys/MixtureFitting: Fitting of Univariate Mixture Distributions to Data using Various Approaches

Estimate von Mises Mixture parameters using Expectation Maximization.

Description

Estimates parameters for univariate von Mises mixture using Expectation Maximization algorithm. In this version stopping criterion is the difference between log-likelihood estimates of subsequent iterations.

Usage

    vmm_fit_em_by_ll( x, p, epsilon = .Machine$double.eps,
                      debug = FALSE, implementation = "C" )

Arguments

x	data vector
p	initialization vector of 3*n parameters, where n is number of mixture components. Structure of p vector is p = c( A1, A2, ..., An, mu1, mu2, ..., mun, k1, k2, ..., kn ), where Ai is the proportion of i-th component, mui is the center of i-th component and ki is the concentration of i-th component.
epsilon	tolerance threshold for convergence
debug	flag to turn the debug prints on/off.
implementation	flag to switch between C (default) and R implementations.

Value

Vector of mixture parameters, whose structure is the same as of input parameter's p.

Author(s)

Andrius Merkys

References

Banerjee et al. Expectation Maximization for Clustering on Hyperspheres (2003), manuscript, accessible on: https://web.archive.org/web/20130120061240/http://www.lans.ece.utexas.edu/~abanerjee/papers/05/banerjee05a.pdf

merkys/MixtureFitting documentation built on July 5, 2025, 5:43 a.m.