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\name{dissimilarity.object}
\alias{dissimilarity.object}
\title{Dissimilarity Matrix Object}
\description{
Objects of class \code{"dissimilarity"} representing the dissimilarity
matrix of a dataset.
}
\section{GENERATION}{
\code{\link{daisy}} returns this class of objects.
Also the functions \code{pam}, \code{clara}, \code{fanny},
\code{agnes}, and \code{diana} return a \code{dissimilarity} object,
as one component of their return objects.
}
\section{METHODS}{
The \code{"dissimilarity"} class has methods for the following generic
functions: \code{print}, \code{summary}.
}
\value{
The dissimilarity matrix is symmetric, and hence its lower triangle
(column wise) is represented as a vector to save storage space.
If the object, is called \code{do}, and \code{n} the number of
observations, i.e., \code{n <- attr(do, "Size")}, then
for \eqn{i < j <= n}, the dissimilarity between (row) i and j is
\code{do[n*(i-1) - i*(i-1)/2 + j-i]}.
The length of the vector is \eqn{n*(n-1)/2}, i.e., of order \eqn{n^2}.
\code{"dissimilarity"} objects also inherit from class
\code{\link{dist}} and can use \code{dist} methods, in
particular, \code{\link{as.matrix}}, such that \eqn{d_{ij}}{d(i,j)}
from above is just \code{as.matrix(do)[i,j]}.
The object has the following attributes:
\item{Size}{the number of observations in the dataset.}
\item{Metric}{the metric used for calculating the
dissimilarities. Possible values are "euclidean", "manhattan",
"mixed" (if variables of different types were present in the
dataset), and "unspecified".}
\item{Labels}{optionally, contains the labels, if any, of the
observations of the dataset.}
\item{NA.message}{optionally, if a dissimilarity could not be
computed, because of too many missing values for some observations
of the dataset.}
\item{Types}{when a mixed metric was used, the types for each
variable as one-letter codes, see also \code{type} in \code{\link{daisy}()}:
% that was confusing with its "T": (as in the book, e.g. p.54):
\describe{
\item{\code{A}: }{Asymmetric binary}
\item{\code{S}: }{Symmetric binary}
\item{\code{N}: }{Nominal (factor)}
\item{\code{O}: }{Ordinal (ordered factor)}
\item{\code{I}: }{Interval scaled, possibly after log transform
\code{"logratio"} (numeric)}
\item{\code{T}: }{ra\bold{T}io treated as \code{\link{ordered}}}
}}
}
\seealso{
\code{\link{daisy}}, \code{\link{dist}},
\code{\link{pam}}, \code{\link{clara}}, \code{\link{fanny}},
\code{\link{agnes}}, \code{\link{diana}}.
}
%\examples{} --> ./daisy.Rd
\keyword{cluster}
|
