View source: R/ZoomStar.r View source: R/ZoomStar.r
zoomStar | R Documentation |
plot in a form of zoom star chart for symbolic object described by interval-valued, multivalued and modal variables
zoomStar(table.Symbolic, j, variableSelection=NULL, offset=0.2,
firstTick=0.2, labelCex=.8, labelOffset=.7, tickLength=.3, histWidth=0.04,
histHeight=2, rotateLabels=TRUE, variableCex=NULL)
table.Symbolic |
symbolic data table |
j |
symbolic object number in symbolic data table used to create the chart |
variableSelection |
numbers of symbolic variables describing symbolic object used to create the chart, if NULL all variables are used |
offset |
relational offset of chart (margin size) |
firstTick |
place of first tick (relational to lenght of axis) |
labelCex |
labels cex parameter of labels |
labelOffset |
relational offset of labels |
tickLength |
relational length of single tick of axis |
histWidth |
histogram (for modal variables) relational width |
histHeight |
histogram (for modal variables) relational heigth |
rotateLabels |
if TRUE labels are rotated due to rotation of axes |
variableCex |
cex parameter of names of variables |
zoom star chart for selected symbolic object in which each axis represents a symbolic variable. Depending on the type of symbolic variable their implementations are presented as:
a) rectangle - interval range of interval-valued variable),
b) circles - categories of multinominal (or multinominal with weights) variable from among coloured circles means categories of the variable observed for the selected symbolic object
bar chart - additional chart for multinominal with weights variable in which each bar represents a weight (percentage share) of a category of the variable
Andrzej Dudek andrzej.dudek@ue.wroc.pl, Justyna Wilk Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland
Bock, H.H., Diday, E. (eds.) (2000), Analysis of symbolic data. Explanatory methods for extracting statistical information from complex data, Springer-Verlag, Berlin.
Diday, E., Noirhomme-Fraiture, M. (eds.) (2008), Symbolic Data Analysis with SODAS Software, John Wiley & Sons, Chichester.
plotInterval
in clusterSim
# LONG RUNNING - UNCOMMENT TO RUN
# Example 1
#data("cars",package="symbolicDA")
#sdt<-cars
#zoomStar(sdt, j=12)
# Example 2
#data("cars",package="symbolicDA")
#sdt<-cars
#variables<-as.matrix(sdt$variables)
#indivN<-as.matrix(sdt$indivN)
#dist<-as.matrix(dist_SDA(sdt))
#classes<-DClust(dist, cl=5, iter=100)
#for(i in 1:max(classes)){
#getOption("device")()
#zoomStar(sdt, .medoid2(dist, classes, i))}
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