# clustermap

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##### Classification of dataset using kmeans or hclust algorithm and representation of clusters on a map.

The function clustermap() performs a classification of the sites from the variables called in names.var and computes a bar plot of the clusters calculated. Classification methods come from hclust() (hierarchical cluster analysis) and kmeans() (k-means clustering) and number of class is chosen with clustnum.

Keywords
multivariate, spatial
##### Usage
clustermap(sp.obj, names.var, clustnum, method=c("kmeans","hclust"), type=NULL,
centers=NULL, scale=FALSE, names.arg="", names.attr=names(sp.obj), criteria=NULL,
carte=NULL, identify=FALSE, cex.lab=0.8, pch=16, col="lightblue3",
xlab="Cluster", ylab="Number", axes=FALSE, lablong="", lablat="")
##### Arguments
sp.obj
object of class extending Spatial-class
names.var
a vector of character; attribute names or column numbers in attribute table
clustnum
integer, number of clusters
method
two methods : kmeans' by default or hclust'
type
If method=hclust', type=complete' by default (the possibilities are given in help(hclust) as ward', single', etc). If method=kmeans', type="Hartigan-Wong" by default (the possibilities are given in help(kmeans) as Forgy', etc)
centers
If method='kmeans', user can give a matrix with initial cluster centers.
scale
If scale=TRUE, the dataset is reducted.
names.arg
a vector of character, names of cluster
names.attr
names to use in panel (if different from the names of variable used in sp.obj)
criteria
a vector of boolean of size the number of spatial units, which permit to represent preselected sites with a cross, using the tcltk window
carte
matrix with 2 columns for drawing spatial polygonal contours : x and y coordinates of the vertices of the polygon
identify
if not FALSE, identify plotted objects (currently only working for points plots). Labels for identification are the row.names of the attribute table row.names(as.data.frame(sp.obj)).
cex.lab
character size of label
pch
a vector of symbol which must be equal to the number of group else all sites are printed in pch[1]
col
a vector of colors which must be equal to the number of group else all sites and all bars are printed in col[1]
xlab
a title for the graphic x-axis
ylab
a title for the graphic y-axis
axes
a boolean with TRUE for drawing axes on the map
lablong
name of the x-axis that will be printed on the map
lablat
name of the y-axis that will be printed on the map
##### Details

The two windows are interactive : the sites selected by a bar chosen on the bar plot are represented on the map in red and the values of sites selected on the map by points' or polygon' are represented in red on the bar plot. The dendogram is also drawn for 'hclust' method. In option, possibility to choose the classification method.

##### Value

In the case where user click on save results button, a list is created as a global variable in last.select object. obs, a vector of integer, corresponds to the number of spatial units selected just before leaving the Tk window, vectclass, vector of integer, corresponds to the number of cluster attributed to each spatial unit.

##### Note

To use the functions hclust and kmeans, we take many arguments by default. If the user would like to modify these arguments, he should call these functions first and then use the function barmap to visualize the calculated clusters.

##### References

Thibault Laurent, Anne Ruiz-Gazen, Christine Thomas-Agnan (2012), GeoXp: An R Package for Exploratory Spatial Data Analysis. Journal of Statistical Software, 47(2), 1-23.

Murtagh, F (1985). Multidimensional Clustering Algorithms.

Hartigan, J. A. and Wong, M. A. (1979). A K-means clustering algorithm. Applied Statistics 28, 100-108

Roger S.Bivand, Edzer J.Pebesma, Virgilio Gomez-Rubio (2009), Applied Spatial Data Analysis with R, Springer.

barmap, pcamap

• clustermap
##### Examples
#####
# data columbus
require("maptools")
example(columbus)

# a basic example using the kmeans method
clustermap(columbus, c("HOVAL","INC","CRIME","OPEN","PLUMB","DISCBD"), 3,
criteria=(columbus@data$CP==1), identify=TRUE, cex.lab=0.7) # example using the hclust method clustermap(columbus,c(7:12), 3, method="hclust", criteria=(columbus@data$CP==1),col=colors()[20:22],identify=TRUE,
cex.lab=0.7, names.arg=c("Group 1","Group 2","Group 3"), xlab="Cluster")

Documentation reproduced from package GeoXp, version 1.6.2, License: GPL (>= 2)

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