r2gtr: Write to gtr, atr, cdt file format

In ctc: Cluster and Tree Conversion.

Description

Write data frame and hclust object to gtr atr, cdt files (Xcluster or Cluster output). Visualisation of cluster can be done with tools like treeview

Usage

r2gtr(hr,file="cluster.gtr",distance=hr$dist.method,dec='.',digits=5)
r2atr(hc,file="cluster.atr",distance=hc$dist.method,dec='.',digits=5)
r2cdt(hr,hc,data,labels=FALSE,description=FALSE,file="cluster.cdt",dec='.')

Arguments

file	the path of the file
data	a matrix (or data frame) which provides the data to put into the file
hr,hc	objects of class hclust (rows and columns)
distance	The distance measure used. This must be one of ‘"euclidean"’, ‘"maximum"’, ‘"manhattan"’, ‘"canberra"’ or ‘"binary"’. Any unambiguous substring can be given.
digits	number digits for precision
labels	a logical value indicating whether we use the frist column as labels (NAME column for cluster file)
description	a logical value indicating whether we use the second column as description (DESCRIPTION column for cluster file)
dec	the character used in the file for decimal points

Details

Function hclust2treeview compute hierarchical clustering and export to all files at once.

Author(s)

Antoine Lucas, http://antoinelucas.free.fr/ctc

References

Antoine Lucas and Sylvain Jasson, Using amap and ctc Packages for Huge Clustering, R News, 2006, vol 6, issue 5 pages 58-60.

See Also

r2xcluster, xcluster2r,hclust,hcluster

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

# use library stats
# Cluster columns
hc <- hclust(dist(t(m)))
# Cluster rows
hr <- hclust(dist(m))

# Export files
r2atr(hc,file="cluster.atr")
r2gtr(hr,file="cluster.gtr")
r2cdt(hr,hc,m ,file="cluster.cdt")

ctc documentation built on Nov. 8, 2020, 5:11 p.m.