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\name{mle.wrappednormal}
\title{Wrapped Normal Maximum Likelihood Estimates}
\alias{mle.wrappednormal}
\alias{print.mle.wrappednormal}
\description{
Computes the maximum likelihood estimates for the parameters of a
Wrapped Normal distribution: mean and concentration parameter.
}
\usage{
mle.wrappednormal(x, mu = NULL, rho = NULL, sd = NULL, K = NULL,
tol = 1e-05, min.sd = 1e-3, min.k = 10, max.iter = 100,
verbose = FALSE, control.circular=list())
\method{print}{mle.wrappednormal}(x, digits = max(3, getOption("digits") - 3), \dots)
}
\arguments{
\item{x}{a vector. The object is coerced to class
\code{\link{circular}}.}
\item{mu}{if \code{NULL} the maximum likelihood estimate of the mean
direction is calculated, otherwise the value is coerced to an object of class \code{circular}.}
\item{rho}{if \code{NULL} the maximum likelihood estimate of the
concentration parameter is calculated.}
\item{sd}{standard deviation of the (unwrapped) normal. Used as an
alternative parametrization.}
\item{K}{number of terms to be used in approximating the density.}
\item{tol}{precision of the estimation.}
\item{min.sd}{minimum value should be reached by the search procedure
for the standard deviation parameter.}
\item{min.k}{minimum number of terms used in approximating the density.}
\item{max.iter}{maximum number of iterations.}
\item{verbose}{logical, if \code{TRUE} information on the convergence
process are printed.}
\item{control.circular}{the attribute of the resulting objects (\code{mu})}
\item{digits}{integer indicating the precision to be used.}
\item{\dots}{further arguments passed to or from other methods.}
}
\value{
Returns a list with the following components:
\item{call}{the \code{\link[base]{match.call}} result.}
\item{mu}{the estimate of the mean direction or the value supplied as an object of class \code{circular}.}
\item{rho}{the estimate of the concentration parameter or the
value supplied}
\item{sd}{the estimate of the standard deviation or the value supplied.}
\item{est.mu}{TRUE if the estimator is reported.}
\item{est.rho}{TRUE if the estimator is reported.}
\item{convergence}{TRUE if the convergence is achieved.}
}
\author{Claudio Agostinelli with a bug fix by Ana Nodehi}
\references{
Jammalamadaka, S. Rao and SenGupta, A. (2001). Topics in Circular
Statistics, Section 4.2.1, World Scientific Press, Singapore.
}
\seealso{
\code{\link{mean.circular}}
}
\examples{
x <- rwrappednormal(n=50, mu=circular(0), rho=0.5)
mle.wrappednormal(x) # estimation of mu and rho (and sd)
mle.wrappednormal(x, mu=circular(0)) # estimation of rho (and sd) only
}
\keyword{htest}
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