```
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(...)

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.

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)) # }