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
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
|
\name{xcluster2r}
\alias{xcluster2r}
\title{Importing Xcluster/Cluster output}
\description{Converting Xcluster/Cluster output (.gtr or .atr) to R hclust file}
\usage{
xcluster2r(file,distance="euclidean",labels=FALSE,fast=FALSE,clean=FALSE,
dec='.')
}
\arguments{
\item{file}{the path of a \emph{Xcluster/Cluster} file (.gtr or .atr)}
\item{distance}{The distance measure used with \emph{Xcluster/Cluster}. This must be one of
\code{"euclidean"}, \code{"pearson"} or \code{"notcenteredpearson"}.
Any unambiguous substring can be given.}
\item{labels}{a logical value indicating whether we use
labels values (in the .cdt file) or not.}
\item{fast}{a logical value indicating whether we reorganize data
like R (\code{Fast=FALSE}) or we let them like \emph{Xcluster/Cluster} did}
\item{clean}{a logical value indicating whether you want the true
distances (\code{clean=FALSE}), or you want a clean dendrogram (see
details below).}
\item{dec}{the character used in the file for decimal points}
}
\details{
See \bold{xcluster} for more details.
}
\value{
An object of class \bold{hclust} which describes the
tree produced by the clustering process.
}
\note{
\emph{Xcluster} is a C program made by \emph{Gavin Sherlock} that performs
hierarchical clustering, K-means and SOM.
\emph{Xcluster} is copyrighted.
To get or have information
about
\emph{Xcluster}: \url{http://genome-www.stanford.edu/~sherlock/cluster.html}
\emph{Cluster} is a program made by \emph{Michael Eisen} that performs
hierarchical clustering, K-means and SOM.
\emph{Cluster} is copyrighted.
To get or have information
about
\emph{Cluster}: \url{http://rana.lbl.gov/EisenSoftware.htm}
}
\references{
Antoine Lucas and Sylvain Jasson, \emph{Using amap and ctc Packages
for Huge Clustering}, R News, 2006, vol 6, issue 5 pages 58-60.
}
\examples{
# Create data
set.seed(1)
m <- matrix(rep(1,3*24),ncol=3)
m[9:16,3] <- 3 ; m[17:24,] <- 3 #create 3 groups
m <- m+rnorm(24*3,0,0.5) #add noise
m <- floor(10*m)/10 #just one digits
r2xcluster(m)
# And once you have Xcluster program:
#
#system('Xcluster -f xcluster.txt -e 0 -p 0 -s 0 -l 0')
#h <- xcluster2r('xcluster.gtr')
#plot(h,hang=-1)
}
\keyword{file}
\author{Antoine Lucas, \url{http://mulcyber.toulouse.inra.fr/projects/amap/}}
\seealso{\code{\link{xcluster}}, \code{\link{r2xcluster}}, \code{\link[stats]{hclust}}, \code{\link[amap]{hcluster}}}
|
