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\name{predict.MclustSSC}
\alias{predict.MclustSSC}
\title{Classification of multivariate observations by semi-supervised Gaussian finite mixtures}
\description{Classify multivariate observations based on Gaussian finite mixture models estimated by \code{\link{MclustSSC}}.}
\usage{
\method{predict}{MclustSSC}(object, newdata, \dots)
}
\arguments{
\item{object}{an object of class \code{'MclustSSC'} resulting from a call to \code{\link{MclustSSC}}.}
\item{newdata}{a data frame or matrix giving the data. If missing the train data obtained from the call to \code{\link{MclustSSC}} are classified.}
\item{\dots}{further arguments passed to or from other methods.}
}
% \details{}
\value{
Returns a list of with the following components:
\item{classification}{a factor of predicted class labels for \code{newdata}.}
\item{z}{a matrix whose \emph{[i,k]}th entry is the probability that
observation \emph{i} in \code{newdata} belongs to the \emph{k}th class.}
}
\author{Luca Scrucca}
% \note{}
\seealso{\code{\link{MclustSSC}}.}
\examples{
\donttest{
X <- iris[,1:4]
class <- iris$Species
# randomly remove class labels
set.seed(123)
class[sample(1:length(class), size = 120)] <- NA
table(class, useNA = "ifany")
clPairs(X, ifelse(is.na(class), 0, class),
symbols = c(0, 16, 17, 18), colors = c("grey", 4, 2, 3),
main = "Partially classified data")
# Fit semi-supervised classification model
mod_SSC <- MclustSSC(X, class)
pred_SSC <- predict(mod_SSC)
table(Predicted = pred_SSC$classification, Actual = class, useNA = "ifany")
X_new = data.frame(Sepal.Length = c(5, 8),
Sepal.Width = c(3.1, 4),
Petal.Length = c(2, 5),
Petal.Width = c(0.5, 2))
predict(mod_SSC, newdata = X_new)
}
}
\keyword{classification}
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