regmixEM.lambda.rd
\name{regmixEM.lambda}
\title{EM Algorithm for Mixtures of Regressions with Local Lambda Estimates}
\alias{regmixEM.lambda}
\usage{
regmixEM.lambda(y, x, lambda = NULL, beta = NULL, sigma = NULL,
k = 2, addintercept = TRUE, arbmean = TRUE,
arbvar = TRUE, epsilon = 1e-8, maxit = 10000,
verb = FALSE)
}
\description{
Returns output for one step of an EM algorithm output for mixtures of multiple regressions
where the mixing proportions are estimated locally.
}
\arguments{
\item{y}{An n-vector of response values.}
\item{x}{An nxp matrix of predictors. See \code{addintercept} below.}
\item{lambda}{An nxk matrix of initial local values of mixing proportions.
Entries should sum to 1. This determines number of components.
If NULL, then \code{lambda} is simply one over the number of components.}
\item{beta}{Initial value of \code{beta} parameters. Should be a pxk matrix,
where p is the number of columns of x and k is number of components.
If NULL, then \code{beta} has uniform standard normal entries. If both
\code{lambda} and \code{beta} are NULL, then number of components is determined by \code{sigma}.}
\item{sigma}{k-vector of initial global values of standard deviations.
If NULL, then \eqn{1/\code{sigma}^2} has random standard exponential entries.
If \code{lambda}, \code{beta}, and \code{sigma} are NULL, then number of components is determined by \code{k}.}
\item{k}{The number of components. Ignored unless all of \code{lambda}, \code{beta},
and \code{sigma} are NULL.}
\item{addintercept}{If TRUE, a column of ones is appended to the x
matrix before the value of p is calculated.}
\item{arbmean}{If TRUE, each mixture component is assumed to have a different set of regression coefficients
(i.e., the \code{beta}s).}
\item{arbvar}{If TRUE, each mixture component is assumed to have a different \code{sigma}.}
\item{epsilon}{The convergence criterion.}
\item{maxit}{The maximum number of iterations.}
\item{verb}{If TRUE, then various updates are printed during each iteration of the algorithm.}
}
\value{
\code{regmixEM.lambda} returns a list of class \code{mixEM} with items:
\item{x}{The set of predictors (which includes a column of 1's if \code{addintercept} = TRUE).}
\item{y}{The response values.}
\item{lambda}{The inputted mixing proportions.}
\item{beta}{The final regression coefficients.}
\item{sigma}{The final standard deviations. If \code{arbmean} = FALSE, then only the smallest standard
deviation is returned. See \code{scale} below.}
\item{scale}{If \code{arbmean} = FALSE, then the scale factor for the component standard deviations is returned.
Otherwise, this is omitted from the output.}
\item{loglik}{The final log-likelihood.}
\item{posterior}{An nxk matrix of posterior probabilities for
observations.}
\item{all.loglik}{A vector of each iteration's log-likelihood.}
\item{restarts}{The number of times the algorithm restarted due to unacceptable choice of initial values.}
\item{ft}{A character vector giving the name of the function.}
}
\details{
Primarily used within \code{regmixEM.loc}.
}
\seealso{
\code{\link{regmixEM.loc}}
}
\examples{
## Compare a 2-component and 3-component fit to NOdata.
data(NOdata)
attach(NOdata)
out1<-regmixEM.lambda(Equivalence, NO)
out2<-regmixEM.lambda(Equivalence, NO, k = 3)
c(out1$loglik, out2$loglik)
}
\keyword{file}