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\name{llgmm}
\alias{llgmm}
\docType{methods}
\title{Log-likelihood for Gaussian Mixture}
\description{
Calculates log-likelihood for a given data vector using a Gaussian mixture distribution.
}
\usage{
llgmm( x, p, implementation = "C" )
}
\arguments{
\item{x}{data vector}
\item{p}{
parameter 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}, sigma1, sigma2, ..., sigma\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
sigma\emph{i} is the scale of \emph{i}-th component.
}
\item{implementation}{
flag to switch between C (default) and R implementations.
}
}
\value{log-likelihood}
\author{Andrius Merkys}
|
