Plotting Eight Direction Arranged Maps or Self-Organizing Maps
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", plot.data.column = NA, log.classes = FALSE, revert.colors = FALSE, ...) level_shardsplot(object, par.names, rows = 1:NCOL(object$data), centers = rep(NA, length(par.names)), class.labels = NA, revert.colors = rep(FALSE, length(par.names)), log.classes = rep(FALSE, length(par.names)), centeredcolors = colorRamp(c("red", "white", "blue")), mfrow = c(2, 2), plot.type = c("eight", "four", "points", "n"), expand = 1, stck = TRUE, grd = FALSE, standardize = FALSE, label = FALSE, plot = TRUE, vertices = TRUE, classcolors = "topo", wghts = 0, xlab = "Dimension 1", ylab = "Dimension 2", xaxs = "i", yaxs = "i", ...) ## S3 method for class 'EDAM' plot(...)
object: an object of class EDAM or som.
par.names: names used to lable the data columns
rows: vector with indices of colomns to be plotted
centers: vector of type numeric defining the class centers for the data. NA if data does not have a center.
class.labels: matrix of type text and dimension(3, NROW(object$data)) defining the lables to be used for maximum, minimum and central value.
centeredcolors: colors to represent the classes with a central value
mfrow: parameter defining number of plots on a page. see par
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.
plot.data.column: column index defining from data.or providing the data used to calculate the coloring of the cells.
log.classes: boolean indicating that the data should be transformed with the logarithmic function before calculating the cell coloring
revert.colors: boolean indicating that the colorscale should be reverted.
level_shardsplot uses multiple shardsplot representations of a SOM in order to depict how the data used to calculate the SOM is distribution across the map. Two representations are possible for the data, first with a single color ramp from the minimum value to the maximum value. The second representation is usefull for data for which a basic value exists some where between minimum and maximum for which a special color representation should be used (e.g. 0 is indicated with white).
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
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.
Nils Raabe, level_shardsplot function from Dominik Reusser
EDAM, TopoS, som
# 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) level_shardsplot(irissom, par.names = names(iris), class.labels = NA, mfrow = c(2,2))