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
55
|
\name{uncerPlot}
\alias{uncerPlot}
\title{
Uncertainty Plot for Model-Based Clustering
}
\description{
Displays the uncertainty in converting a conditional probablility from EM
to a classification in model-based clustering.
}
\usage{
uncerPlot(z, truth, \dots)
}
\arguments{
\item{z}{
A matrix whose \emph{[i,k]}th entry is the
conditional probability of the ith observation belonging to
the \emph{k}th component of the mixture.
}
\item{truth}{
A numeric or character vector giving the true classification of the data.
}
\item{\dots }{
Provided to allow lists with elements other than the arguments can
be passed in indirect or list calls with \code{do.call}.
}
}
\value{
A plot of the uncertainty profile of the data,
with uncertainties in increasing order of magnitude.
If \code{truth} is supplied and the number of
classes is the same as the number of columns of
\code{z}, the uncertainty
of the misclassified data is marked by vertical lines on the plot.
}
\details{
When \code{truth} is provided and the number of classes is compatible
with \code{z}, the function \code{compareClass} is used to to find best
correspondence between classes in \code{truth} and \code{z}.
}
\seealso{
\code{\link{mclustBIC}},
\code{\link{em}},
\code{\link{me}},
\code{\link{mapClass}}
}
\examples{
irisModel3 <- Mclust(iris[,-5], G = 3)
uncerPlot(z = irisModel3$z)
uncerPlot(z = irisModel3$z, truth = iris[,5])
}
\keyword{cluster}
% docclass is function
|
