graphscan-plot: Plot clusters localisations or the 1D events distributions.

In graphscan: Cluster Detection with Hypothesis Free Scan Statistic

Description

plot clusters localisations in a 1D, 2D or 3D space. In 1D, the distribution (in frequencies) of number of events on each position can be ploted.

Usage

graphscan_plot(x,events_series=1,map=NULL,indice="cucala",
sphere=TRUE,projection=FALSE,...)

Arguments

x	a graphscan object containing a cluster analysis.
events_series	a numeric or character vector containing 1D cluster identifiants of the events series to draw. If 'events_series="all"' the distribution of number of clusters on each position is plotted.
map	a 'SpatialPolygons' or 'SpatialPolygonsDataFrame' object to add to the 2D graph of clusters localisations (generally the outline of the studied region).
indice	a character string used in nD, to define the type of index to draw. Possible values are "cucala" (default) and "kulldorff".
sphere	a boolean ("TRUE" by default) to define if the spheres used to represent the 3D envelope of the cluster are drawn.
projection	a boolean ("FALSE" by default) to draw a projection in 2D of a 3D cluster. Three plots are drawn respectively for 'y vs x', 'z vs x' and 'z vs y'.
...	further arguments passed to or from other methods.

Details

To draw the distribution of number of events on each position in 1D, the events series must be of same length and aligned. The 3D representation of cluster use 'OpenGL' (http://www.opengl.org) an environment for interactive 2D and 3D graphics. If the number of cases points is very important old computers will display graphics quite slowly. In this case, use the option "projection=TRUE".

Author(s)

Robin Loche, Benoit Giron, David Abrial, Lionel Cucala, Myriam Charras-Garrido, Jocelyn De-Goer. Maintainer: David Abrial <[e-mail]>

References

Cucala, L. 2008. A hypothesis-free multiple scan statistic with variable window, Biometrical Journal, 2, p. 299-310.

Cucala, L. 2009. A flexible spatial scan test for case event data, Computational Statistics and Data Analysis, 53, p. 2843-2850.

See Also

barplot

Examples

## Not run: 
# 1D example:
require(ape)
data(woodmouse)
g1<-graphscan_1d(data=woodmouse)
g1<-cluster(g1)
graphscan_plot(g1,events_series=3)
dev.new()
graphscan_plot(g1,events_series="all")

# 2D example:
data(france_two_clusters)
g2<-graphscan_nd(data=france_two_clusters)
g2<-cluster(g2)
graphscan_plot(g2,map=france)

# 3D example:
data(sample3d)
g3<-graphscan_nd(data=sample3d)
g3<-cluster(g3)
graphscan_plot(g3,projection=TRUE) # 2D plot
graphscan_plot(g3) # 3D plot

## End(Not run)

graphscan documentation built on May 1, 2019, 9:15 p.m.