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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$data),
centers = rep(NA, length(par.names)), class.labels = NA,
revert.colors = rep(FALSE, length(par.names)),
log.classes = rep(FALSE, length(par.names)),
centeredcolors = colorRamp(c("red", "white", "blue")),
mfrow = c(2, 2), plot.type = c("eight", "four", "points", "n"),
expand = 1, stck = TRUE, grd = FALSE, standardize = FALSE,
label = FALSE, plot = TRUE, vertices = TRUE, classcolors = "topo",
wghts = 0, xlab = "Dimension 1", ylab = "Dimension 2",
xaxs = "i", yaxs = "i", ...)
# S3 method for EDAM
plot(...)
an object of class EDAM or som.
names used to lable the data columns
vector with indices of colomns to be plotted
vector of type numeric defining the class centers for the data. NA if data does not have a center.
matrix of type text and dimension(3, NROW(object$data)) defining the lables to be used for maximum, minimum and central value.
colors to represent the classes with a central value
parameter defining number of plots on a page. see par
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.
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.
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.
logical. If TRUE (which automatically sets stck to TRUE),
the variation of cells is restricted to their original discrete values.
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.
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.
logical. If TRUE, the shards are labeled by the rownames of the preimages.
logical. If FALSE, all graphical output is suppressed.
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.
logical. If TRUE the grid is drawn.
colors to represent the classes, or a character giving the colorscale for the classes.
Since now available scales are rainbow, topo and gray.
an optional vector of length k giving relative weights of the variables
in computing Euclidean distances. Meaningless if object$preimages is a dissimilarity matrix.
see par
see par
see par
see par
further plotting parameters.
column index defining from data.or providing the data used to calculate the coloring of the cells.
boolean indicating that the data should be transformed with the logarithmic function before calculating the cell coloring
boolean indicating that the colorscale should be reverted.
The following list is (invisibly) returned:
the images of the visualized data
the criterion of the visualization
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.
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.
# NOT RUN {
# 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))
# }
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