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\name{mvn}
\alias{mvn}
\title{
Univariate or Multivariate Normal Fit
}
\description{
Computes the mean, covariance, and log-likelihood from fitting a single
Gaussian to given data (univariate or multivariate normal).
}
\usage{
mvn( modelName, data, prior = NULL, warn = NULL, \dots)
}
\arguments{
\item{modelName}{
A character string representing a model name. This can be either
\code{"Spherical"}, \code{"Diagonal"}, or \code{"Ellipsoidal"} or
else \cr
\code{"X"} for one-dimensional data,\cr
\code{"XII"} for a spherical Gaussian, \cr
\code{"XXI"} for a diagonal Gaussian \cr
\code{"XXX"} for a general ellipsoidal Gaussian
}
\item{data}{
A numeric vector, matrix, or data frame of observations. Categorical
variables are not allowed. If a matrix or data frame, rows
correspond to observations and columns correspond to variables.
}
\item{prior}{
Specification of a conjugate prior on the means and variances.
The default assumes no prior.
}
\item{warn}{
A logical value indicating whether or not a warning should be issued
whenever a singularity is encountered.
The default is given by \code{mclust.options("warn")}.
}
\item{\dots }{
Catches unused arguments in indirect or list calls via \code{do.call}.
}
}
\value{
A list including the following components:
\item{modelName}{
A character string identifying the model (same as the input argument).
}
\item{parameters}{
\describe{
\item{\code{mean}}{
The mean for each component. If there is more than one component,
this is a matrix whose kth column is the mean of the \emph{k}th
component of the mixture model.
}
\item{\code{variance}}{
A list of variance parameters for the model.
The components of this list depend on the model
specification. See the help file for \code{\link{mclustVariance}}
for details.
}
}
}
\item{loglik}{
The log likelihood for the data in the mixture model.
}
\item{Attributes:}{
\code{"WARNING"} An appropriate warning if problems are
encountered in the computations.
}
}
\seealso{
\code{\link{mvnX}},
\code{\link{mvnXII}},
\code{\link{mvnXXI}},
\code{\link{mvnXXX}},
\code{\link{mclustModelNames}}
}
\examples{
n <- 1000
set.seed(0)
x <- rnorm(n, mean = -1, sd = 2)
mvn(modelName = "X", x)
mu <- c(-1, 0, 1)
set.seed(0)
x <- sweep(matrix(rnorm(n*3), n, 3) \%*\% (2*diag(3)),
MARGIN = 2, STATS = mu, FUN = "+")
mvn(modelName = "XII", x)
mvn(modelName = "Spherical", x)
set.seed(0)
x <- sweep(matrix(rnorm(n*3), n, 3) \%*\% diag(1:3),
MARGIN = 2, STATS = mu, FUN = "+")
mvn(modelName = "XXI", x)
mvn(modelName = "Diagonal", x)
Sigma <- matrix(c(9,-4,1,-4,9,4,1,4,9), 3, 3)
set.seed(0)
x <- sweep(matrix(rnorm(n*3), n, 3) \%*\% chol(Sigma),
MARGIN = 2, STATS = mu, FUN = "+")
mvn(modelName = "XXX", x)
mvn(modelName = "Ellipsoidal", x)
}
\keyword{cluster}
|
