r2gtr {ctc} | R Documentation |
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
r2gtr(hr,file="cluster.gtr",distance="euclidean",dec='.',digits=5) r2atr(hc,file="cluster.atr",distance="euclidean",dec='.',digits=5) r2cdt(hr,hc,data,labels=FALSE,description=FALSE,file="cluster.cdt",dec='.')
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 |
Antoine Lucas, http://genopole.toulouse.inra.fr/~lucas/R
# Create data .Random.seed <- c(1, 416884367 ,1051235439) 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 library(mva) # 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")