swh:1:snp:dc80812a22a7696ce24055bd58afbf9f13e3e78c
Tip revision: e65e968c8f3853c5ed581ab3833000dc922d7456 authored by Bettina Gruen on 23 February 2011, 00:00:00 UTC
version 2.3-4
version 2.3-4
Tip revision: e65e968
FLXMCmvcombi.Rd
%
% Copyright (C) 2009 Friedrich Leisch and Bettina Gruen
% $Id: FLXMCmvcombi.Rd 4411 2009-09-23 15:03:19Z gruen $
%
\name{FLXMCmvcombi}
\alias{FLXMCmvcombi}
\title{FlexMix Binary and Gaussian Clustering Driver}
\description{
This is a model driver for \code{\link{flexmix}} implementing
model-based clustering of a combination of binary and Gaussian data.
}
\usage{
FLXMCmvcombi(formula = . ~ .)
}
\arguments{
\item{formula}{A formula which is interpreted relative to the formula
specified in the call to \code{\link{flexmix}} using
\code{\link{update.formula}}. Only the left-hand side (response) of
the formula is used. Default is to use the original
\code{\link{flexmix}} model
formula.}
}
\details{
This model driver can be used to cluster mixed-mode binary and
Gaussian data. It checks which columns of a matrix contain only zero
and ones, and does the same as \code{\link{FLXMCmvbinary}} for them. For
the remaining columns of the data matrix independent Gaussian
distributions are used (same as \code{\link{FLXMCmvnorm}} with
\code{diagonal=FALSE}.
The same could be obtained by creating a corresponding list of two
models for the respective columns, but \code{FLXMCmvcombi} does a
better job in reporting parameters.
}
\value{
\code{FLXMCmvcombi} returns an object of class \code{FLXMC}.
}
\author{Friedrich Leisch}
\seealso{\code{\link{flexmix}}, \code{\link{FLXMCmvbinary}}, \code{\link{FLXMCmvnorm}}}
\keyword{cluster}
\examples{
## create some artificial data
x1 <- cbind(rnorm(300),
sample(0:1, 300, replace=TRUE, prob=c(0.25, 0.75)))
x2 <- cbind(rnorm(300, mean=2, sd=0.5),
sample(0:1, 300, replace=TRUE, prob=c(0.75, 0.25)))
x <- rbind(x1, x2)
## fit the model
f1 <- flexmix(x~1, k=2, model=FLXMCmvcombi())
## should be similar to the original parameters
parameters(f1)
table(clusters(f1), rep(1:2, c(300,300)))
## a column with noise should not hurt too much
x <- cbind(x, rnorm(600))
f2 <- flexmix(x~1, k=2, model=FLXMCmvcombi())
parameters(f2)
table(clusters(f2), rep(1:2, c(300,300)))
}