shardsplot {klaR} | R Documentation |
Plotting method for objects of class
EDAM
or som
.
shardsplot(object, plot.type = c("eight", "four", "points", "n"), expand = 1, stck = TRUE, grd = FALSE, standardize = FALSE, data.or = NA, label = FALSE, plot = TRUE, classes = 0, vertices = TRUE, classcolors = "rainbow", wghts = 0, xlab = "Dimension 1", ylab = "Dimension 2", xaxs = "i", yaxs = "i", ...) ## S3 method for class 'EDAM': plot(...)
object |
an object of class EDAM or som . |
plot.type |
a character giving the shape of the shards.
Available are “eight ” and “four ” for octagons resp. rectangles,
and “points ” for points. If plot.type is “n ”,
no shards are plotted at all. |
expand |
a numeric giving the relative expansion of the axes.
A value greater than one implies smaller shards. Varying expand
can be sensible for visual reasons. |
stck |
logical. If TRUE the cells are varied continously corresponding to
the differences of direct neighbors in the origin space.
Within this variation the relative order of the cells is always preserved. |
grd |
logical. If TRUE (which automatically sets stck to TRUE ),
the variation of cells is restricted to their original discrete values. |
standardize |
logical. If TRUE , then the measurements in object$preimages
are standardized before calculating Euclidean distances.
Measurements are standardized for each variable by dividing by the variable's
standard deviation. Meaningless if object$preimages is a dissimilarity matrix. |
data.or |
original data and classes where the first k columns are variables and the (k+1)-th column are the classes.
If defined and class of object is som , data.or is used to assign a class to each codebook. There
a codebook receives the class, from which the majority of its assigned objects origins. |
label |
logical. If TRUE , the shards are labeled by the rownames of the preimages. |
plot |
logical. If FALSE , all graphical output is suppressed. |
classes |
a vector giving alternative classes for objects of class EDAM ; classes have to be given in
the original order of the data to which EDAM was applied. |
vertices |
logical. If TRUE the grid is drawn. |
classcolors |
colors to represent the classes, or a character giving the colorscale for the classes.
Since now available scales are rainbow , topo and gray . |
wghts |
an optional vector of length k giving relative weights of the variables
in computing Euclidean distances. Meaningless if object$preimages is a dissimilarity matrix. |
xaxs |
see par |
yaxs |
see par |
xlab |
see par |
ylab |
see par |
... |
further plotting parameters. |
If plot.type
is “four
” or “eight
”, the shape of each shard depends
on the relative distances of the actual object
or codebook to its up to eight neighbours. If plot.type
is “eight
”, shardsplot
corresponds to the representation method
suggested by Cottrell and de Bodt (1996) for Kohonen Self-Organizing Maps.
If plot.type
is “points
”, shardsplot
reduces to a usual scatter plot.
The following list is (invisibly) returned:
Cells.ex |
the images of the visualized data |
S |
the criterion of the visualization |
Nils Raabe
Cottrell, M., and de Bodt, E. (1996). A Kohonen Map Representation to Avoid Misleading Interpretations. Proceedings of the European Symposium on Atrificial Neural Networks, D-Facto, pp. 103–110.
# Compute clusters and an Eight Directions Arranged Map for the # country data. Plotting the result. data(countries) logcount <- log(countries[,2:7]) sdlogcount <- apply(logcount, 2, sd) logstand <- t((t(logcount) / sdlogcount) * c(1,2,6,5,5,3)) cclasses <- cutree(hclust(dist(logstand)), k = 6) countryEDAM <- EDAM(logstand, classes = cclasses, sa = FALSE, iter.max = 10, random = FALSE) plot(countryEDAM, vertices = FALSE, label = TRUE, stck = FALSE) # Compute and plot a Self-Organizing Map for the iris data data(iris) library(som) irissom <- som(iris[,1:4], xdim = 6, ydim = 14) shardsplot(irissom, data.or = iris, vertices = FALSE) opar <- par(xpd = NA) legend(7.5, 6.1, col = rainbow(3), xjust = 0.5, yjust = 0, legend = levels(iris[, 5]), pch = 16, horiz = TRUE) par(opar)