1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
|
\name{predict.Mclust}
\alias{predict.Mclust}
\title{Cluster multivariate observations by Gaussian finite mixture modeling}
\description{Cluster prediction for multivariate observations based on Gaussian finite mixture models estimated by \code{\link{Mclust}}.}
\usage{
\method{predict}{Mclust}(object, newdata, \dots)
}
\arguments{
\item{object}{an object of class \code{'Mclust'} resulting from a call to \code{\link{Mclust}}.}
\item{newdata}{a data frame or matrix giving the data. If missing the clustering data obtained from the call to \code{\link{Mclust}} 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 cluster 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 cluster.}
}
\author{Luca Scrucca}
% \note{}
\seealso{\code{\link{Mclust}}.}
\examples{
model <- Mclust(faithful)
# predict cluster for the observed data
pred <- predict(model)
str(pred)
pred$z # equal to model$z
pred$classification # equal to
plot(faithful, col = pred$classification, pch = pred$classification)
# predict cluster over a grid
grid <- apply(faithful, 2, function(x) seq(min(x), max(x), length = 50))
grid <- expand.grid(eruptions = grid[,1], waiting = grid[,2])
pred <- predict(model, grid)
plot(grid, col = mclust.options("classPlotColors")[pred$classification], pch = 15, cex = 0.5)
points(faithful, pch = model$classification)
}
\keyword{multivariate}
|
