mix.rd

这个包里面含有利用极大似然估计方法来拟合成组数据和条件数据的有限混合分布模型的函数

\name{mix} 
\alias{mix}
\title{Estimate Parameters of Mixture Distributions}
\description{
  Find a set of overlapping component distributions that
  gives the best fit to grouped data and conditional
  data, using a combination of a Newton-type method and
  EM algorithm.
}
\usage{
mix(mixdat, mixpar, dist = "norm", constr = list(conpi = "NONE", 
    conmu = "NONE", consigma = "NONE", fixpi = NULL, fixmu = NULL, 
    fixsigma = NULL, cov = NULL, size = NULL), emsteps = 1, 
    usecondit = FALSE, exptol = 5e-06, print.level = 0, \dots) 
}
\arguments{
\item{mixdat}{A data frame containing grouped data, whose first
 column should be right boundaries of grouping intervals where
 the first and last intervals are open-ended; whose second
 column should consist of the frequencies indicating numbers of
 observations falling into each interval. If conditional data
 are available, this data frame should have \emph{k} + 2 columns, where
 \emph{k} is the number of components, whose element in row \emph{j} and
 column \emph{i} + 2 is the number of observations from the \emph{j}th interval
 belonging to the \emph{i}th component.}
\item{mixpar}{A data frame containing starting values for
 parameters of component distributions, which are, in order, the
 proportions, means, and standard deviations.}
\item{dist}{the distribution of components, it can be one of 
  \code{"norm"}, \code{"lnorm"}, \code{"gamma"}, \code{"weibull"}, 
  \code{"binom"}, \code{"nbinom"} and \code{"pois"}.}
\item{constr}{a list of constraints on parameters of
 component distributions. See function \code{\link{mixconstr}}.}
\item{emsteps}{a non-negative integer specifying the number of EM steps 
  to be performed.}
\item{usecondit}{logical. If \code{usecondit} is \code{TRUE} and \code{mixdat}
 includes conditional data, then conditional data will be used with
 grouped data to estimate parameters of mixtures.}
\item{exptol}{a positive scalar giving the tolerance at which the
  scaled fitted value is considered large enough to be a degree
  of freedom.}
\item{print.level}{this argument determines the level of printing
  which is done during the optimization process.  The default
  value of \code{0} means that no printing occurs, a value of \code{1}
  means that initial and final details are printed and a value
  of \code{2} means that full tracing information is printed.}
\item{\dots}{additional arguments to the optimization function
\code{\link{nlm}}}.
}
\value{
A list containing the following items:
\item{parameters}{A data frame containing estimated values for
 parameters of component distributions, which are, in order, 
 the proportions, means, and standard deviations.}
\item{se}{A data frame containing estimated values for
 standard errors of parameters of component distributions.}
\item{distribution}{the distribution used to fit the data.}
\item{constraint}{the constraints on parameters.}
\item{chisq}{the goodness-of-fit chi-square statistic.}
\item{df}{degrees of freedom of the fitted mixture model.}
\item{P}{a significance level (P-value) for the goodness-of-fit test.}
\item{vmat}{covariance matrix for the estimated parameters.}
\item{mixdata}{the original data, i.e. the argument \code{mixdat}.}
\item{usecondit}{the value of the argument \code{usecondit}.}
}
\references{
  Macdonald, P.D.M. and Green, P.E.J. (1988) \emph{User's Guide to Program MIX: 
  An Interactive Program for Fitting Mixtures of Distributions.} ICHTHUS DATA
  SYSTEMS.
}
\seealso{
 \code{\link{mixgroup}} for grouping data, \code{\link{mixparam}} for
 organizing the parameter values, \code{\link{mixconstr}} for
 constructing constraints. \code{\link[base]{nlm}} for additional
 arguments.
}
\examples{
data(pike65)
data(pikepar)
fitpike1 <- mix(pike65, pikepar, "lnorm", constr = mixconstr(consigma = "CCV"), emsteps = 3)
fitpike1
plot(fitpike1)
data(pike65sg)
fitpike2 <- mix(pike65sg, pikepar, "lnorm", emsteps = 3, usecondit = TRUE)
fitpike2
plot(fitpike2)
data(bindat)
data(binpar)
fitbin1 <- mix(bindat, binpar, "binom", constr = mixconstr(consigma = "BINOM", size = c(20, 20, 20, 20)))
plot(fitbin1)
fitbin2 <- mix(bindat, binpar, "binom", constr = mixconstr(conpi = "PFX",
               fixpi = c(TRUE, TRUE, TRUE, TRUE), consigma = "BINOM", size = c(20, 20, 20, 20)))
plot(fitbin2)
}
\keyword{models}