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\name{cmm_fit_em}
\alias{cmm_fit_em}
\docType{methods}
\title{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{
\item{x}{data vector}
\item{p}{
initialization vector of 3*\emph{n} parameters, where \emph{n} is
number of mixture components. Structure of p vector is
p = c( A1, A2, ..., A\emph{n}, mu1, mu2, ..., mu\emph{n}, gamma1, gamma2, ..., gamma\emph{n} ),
where A\emph{i} is the proportion of \emph{i}-th component,
mu\emph{i} is the center of \emph{i}-th component and
gamma\emph{i} is the Cauchy scale of \emph{i}-th component.
}
\item{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.
}
\item{iter.cauchy}{
number of iterations to fit a single Cauchy component.
}
\item{debug}{
flag to turn the debug prints on/off.
}
\item{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.
}
\references{
Ferenc Nahy. Parameter Estimation of the Cauchy Distribution in
Information Theory Approach (2006).
Journal of Universal Computer Science
}
\author{Andrius Merkys}
|
