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\name{ddalphaf.classify}
\alias{ddalphaf.classify}
\alias{predict.ddalphaf}
\title{
Classify using Functional DD-Classifier
}
\description{
Classifies data using the functional DD-classifier.
}
\usage{
ddalphaf.classify(ddalphaf, objectsf, subset, ...)
\method{predict}{ddalphaf}(object, objectsf, subset, ...)
}
\arguments{
\item{ddalphaf, object}{
Functional DD-classifier (obtained by \code{\link{ddalphaf.train}}).
}
\item{objectsf}{list containing lists (functions) of two vectors of equal length, named "args" and "vals": arguments sorted in ascending order and corresponding them values respectively
}
\item{subset}{
an optional vector specifying a subset of observations to be classified.
}
\item{\dots}{
additional parameters, passed to the classifier, selected with parameter \code{classifier.type} in \code{\link{ddalphaf.train}}.
}
}
\value{
List containing class labels.
}
\references{
Mosler, K. and Mozharovskyi, P. (2017). Fast DD-classification of functional data. \emph{Statistical Papers} \bold{58} 1055--1089.
Mozharovskyi, P. (2015). \emph{Contributions to Depth-based Classification and Computation of the Tukey Depth}. Verlag Dr. Kovac (Hamburg).
}
\seealso{
\code{\link{ddalphaf.train}} to train the functional DD\eqn{\alpha}-classifier.
}
\examples{
\dontrun{
## load the Growth dataset
dataf = dataf.growth()
learn = c(head(dataf$dataf, 49), tail(dataf$dataf, 34))
labels= c(head(dataf$labels, 49), tail(dataf$labels, 34))
test = tail(head(dataf$dataf, 59), 10) # elements 50:59. 5 girls, 5 boys
c = ddalphaf.train (learn, labels, classifier.type = "ddalpha")
classified = ddalphaf.classify(c, test)
print(unlist(classified))
}
}
\keyword{ functional }
\keyword{ robust }
\keyword{ multivariate }
\keyword{ nonparametric }
\keyword{ classif }
|
