kohonen (version 1.1.1)

plot.kohonen: Plot kohonen object

Description

Plot self-organising map, obtained from function kohonen. Several types of plots are supported.

Usage

plot.kohonen(x, type = c("changes", "codes", "counts", "mapping",
                         "prediction", "property"),
             classif, labels=NULL, pchs=NULL, main=NULL,
             palette.name = heat.colors, ncolors, 
             zlim=NULL, property, heatkey=TRUE, contin, bgcol=NULL,
             ...)

Arguments

x

kohonen object.

type

type of plot. (Wow!)

classif

classification object, as returned by predict.kohonen, or vector of unit numbers. Only needed if type equals "mapping" and "counts".

labels

labels to plot when type equals "mapping".

pchs

symbols to plot when type equals "mapping".

main

title of the plot.

palette.name

colors to use as unit background for "codes", "counts", "prediction", and "property" plotting types.

ncolors

number of colors to use. Default is 20 for continuous data, and the number of distinct values (if less than 20) for class data.

zlim

optional range for color coding of unit backgrounds.

property

values to use with the "property" plotting type.

heatkey

whether or not to generate a heatkey at the left side of the plot in the "property" and "counts" plotting types.

contin

whether or not the data should be seen as discrete (i.e. classes) or continuous in nature. Only relevant for the colour keys of plots of supervised networks.

bgcol

optional argument to colour the unit backgrounds for the "mapping" and "codes" plotting type. Defaults to "gray" and "transparent" in both types, respectively.

...

other graphical parameters, e.g. colours of labels, or plotting symbols, in the "mapping" plotting type.

Details

Several different types of plots are supported: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

See Also

som, bdk, xyf

Examples

data(wines)
set.seed(7)

kohmap <- xyf(scale(wines), classvec2classmat(wine.classes),
              grid = somgrid(5, 5, "hexagonal"), rlen=100)
plot(kohmap, type="changes", main="plot of changes")
plot(kohmap, type="codes", main="codes plot")
plot(kohmap, type="counts", main="counts plot")
plot(kohmap, type="mapping", 
     labels=wine.classes, col=wine.classes,
     main="mapping plot")
xyfpredictions <- predict(kohmap, newdata=kohmap$codes)$classif
bgcols <- c("gray", "pink", "lightgreen")
plot(kohmap, type="mapping", col=wine.classes,
     pchs=wine.classes, bgcol=bgcols[as.integer(xyfpredictions)], 
     main="another mapping plot")

plot(kohmap, type="prediction",
     labels=paste("Variety", 1:3),
     palette.name = rainbow,
     main="unit class prediction", cex=.8)

### Plot mean distance of mapped objects to their unit codebook vector
hits <- sort(unique(kohmap$unit.classif))
distances <- rep(NA, 25)
for (i in seq(along=hits))
  distances[hits[i]] <- mean(kohmap$distances[kohmap$unit.classif == hits[i]])

plot(kohmap, type="property", property=distances,
     main="property plot: mean distances")