cmm_fit_em {MixtureFitting} | R Documentation |
Estimate Cauchy Mixture parameters using Expectation Maximization.
Description
Estimates parameters for Caucy mixture using Expectation Maximization algorithm.
Usage
cmm_fit_em( x, p, epsilon = c( 0.000001, 0.000001, 0.000001 ),
iter.cauchy = 20, 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, gamma1, gamma2, ..., gamman ), where Ai is the proportion of i-th component, mui is the center of i-th component and gammai is the Cauchy scale of i-th component. |
epsilon |
tolerance threshold for convergence. Structure of epsilon is epsilon = c( epsilon_A, epsilon_mu, epsilon_gamma ), where epsilon_A is threshold for component proportions, epsilon_mu is threshold for component centers and epsilon_gamma is threshold for component Cauchy scales. |
iter.cauchy |
number of iterations to fit a single Cauchy component. |
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
Ferenc Nahy. Parameter Estimation of the Cauchy Distribution in Information Theory Approach (2006). Journal of Universal Computer Science