x: multivariate data in form of matrix or data frame
xA: assignment of clusters to the coordinates of the boxes
yA: assignment of clusters to the coordinates of the boxes
zA: assignment of clusters to the coordinates of the boxes
labels: vector of character strings for labeling the plots
locations: locations for the boxes on the plot (e.g. X/Y coordinates)
nrow: integers giving the number of rows ands columns to use when 'locations' is 'NULL'. By default, 'nrow == ncol', a square will be used.
ncol: integers giving the number of rows and columns to use when 'locations' is 'NULL'. By default, 'nrow == ncol', a square will be used.
key.loc: vector with x and y coordinates of the unit key.
key.labels: vector of character strings for labeling the segments of the unit key. If omitted, the second component of 'dimnames(x)' ist used, if available.
key.xpd: clipping switch for the unit key (drawing and labeling), see 'par("xpd")'.
xlim: vector with the range of x coordinates to plot
ylim: vector with the range of y coordinates to plot
flip.labels: logical indicating if the label locations should flip up and down from diagram to diagram. Defaults to a somewhat smart heuristic.
len: multiplicative values for the space used in the plot window
leglen: multiplicative values for the space of the labels on the legend
axes: logical flag: if 'TRUE' axes are added to the plot
frame.plot: logical flag: if 'TRUE', the plot region ist framed
main: a main title for the plot
sub: a sub title for the plot
xlab: a label for the x axis
ylab: a label for the y axis
cex: character expansion factor for the labels
lwd: line width used for drawing
lty: line type used for drawing
xpd: logical or NA indicationg if clipping should be done, see 'par(xpd=.)'
mar: argument to 'par(mar=*)', rypically choosing smaller margings than by default
add: logical, if 'TRUE' add boxes to current plot
plot: logical, if 'FALSE', nothing is plotted
...: further arguments, passed to the first call of 'plot()'
Returns
No return value, creates a plot.
Details
This type of graphical approach for multivariate data is only applicable where the data can be grouped into three clusters. This means that before the plot can be made the data undergo a hierarchical cluster to get the size of each cluster. The distance measure for the hierarchicla cluster is complete linkage. Each cluster represents one side of the boxes.
References
C. Reimann, P. Filzmoser, R.G. Garrett, and R. Dutter: Statistical Data Analysis Explained. Applied Environmental Statistics with R. John Wiley and Sons, Chichester, 2008.
#plots the background and the boxes for the elementsdata(ohorizon)X=ohorizon[,"XCOO"]Y=ohorizon[,"YCOO"]el=log10(ohorizon[,c("Co","Cu","Ni","Rb","Bi","Na","Sr")])data(kola.background)sel <- c(3,8,22,29,32,35,43,69,73,93,109,129,130,134,168,181,183,205,211,218,237,242,276,292,297,298,345,346,352,372,373,386,408,419,427,441,446,490,516,535,551,556,558,564,577,584,601,612,617)x=el[sel,]xwid=diff(range(X))/12e4ywid=diff(range(Y))/12e4plot(X,Y,frame.plot=FALSE,xaxt="n",yaxt="n",xlab="",ylab="",type="n", xlim=c(360000,max(X)))plotbg(map.col=c("gray","gray","gray","gray"),add.plot=TRUE)boxes(x,locations=cbind(X[sel],Y[sel]),len=20000,key.loc=c(800000,7830000),leglen=25000, cex=0.75, add=TRUE, labels=NULL, lwd=1.1)