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\name{emm}
\alias{emm}
\alias{emm.default}
\alias{emm.grouped.data}
\title{Empirical Moments}
\description{
Raw empirical moments for individual and grouped data.
}
\usage{
emm(x, order = 1, \dots)
\method{emm}{default}(x, order = 1, \dots)
\method{emm}{grouped.data}(x, order = 1, \dots)
}
\arguments{
\item{x}{a vector or matrix of individual data, or an object of class
\code{"grouped data"}.}
\item{order}{order of the moment. Must be positive.}
\item{\dots}{further arguments passed to or from other methods.}
}
\details{
Arguments \code{\dots} are passed to \code{\link{colMeans}};
\code{na.rm = TRUE} may be useful for individual data with missing
values.
For individual data, the \eqn{k}th empirical moment is
\eqn{\sum_{j = 1}^n x_j^k}{sum(j; x[j]^k)}.
For grouped data with group boundaries \eqn{c_0, c_1, \dots,
c_r}{c[0], c[1], \dots, c[r]} and group frequencies \eqn{n_1, \dots,
n_r}{n[1], \dots, n[r]}, the \eqn{k}th empirical moment is
\deqn{\frac{1}{n} \sum_{j = 1}^r \frac{n_j (c_j^{k + 1} - c_{j - 1}^{k + 1})}{%
(k + 1) (c_j - c_{j - 1})},}{%
(1/n) * sum(j; (n[j] * {c[j]^(k+1) - c[j-1]^(k+1)})/%
((k+1) * {c[j] - c[j-1]})),}
where \eqn{n = \sum_{j = 1}^r n_j}{n = sum(j; n[j])}.
}
\value{
A named vector or matrix of moments.
}
\seealso{
\code{\link{mean}} and \code{\link{mean.grouped.data}} for simpler
access to the first moment.
}
\references{
Klugman, S. A., Panjer, H. H. and Willmot, G. E. (1998),
\emph{Loss Models, From Data to Decisions}, Wiley.
}
\author{
Vincent Goulet \email{vincent.goulet@act.ulaval.ca} and
Mathieu Pigeon
}
\examples{
## Individual data
data(dental)
emm(dental, order = 1:3)
## Grouped data
data(gdental)
emm(gdental)
x <- grouped.data(cj = gdental[, 1],
nj1 = sample(1:100, nrow(gdental)),
nj2 = sample(1:100, nrow(gdental)))
emm(x) # same as mean(x)
}
\keyword{univar}
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