plot.kohonen {kohonen} | R Documentation |
Plot self-organising map, obtained from function kohonen. Several types of plots are supported.
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, ...)
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 "classifications" and
"counts". |
labels |
labels to plot when type equals
"classifications". |
pchs |
symbols to plot when type equals
"classifications". |
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. |
... |
other graphical parameters, e.g. colours of labels, or plotting symbols, in the "classifications" plotting type. |
Several different types of plots are supported:
property
contains the numerical
values. See examples below.Ron Wehrens
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") plot(kohmap, type="mapping", pchs=wine.classes, col=wine.classes, main="another mapping plot") ### Made-up labels :-) plot(kohmap, type="prediction", labels=c("Chianti", "Barolo", "Montepulciano"), palette.name = rainbow, main="unit class prediction", cex=.8) ### Plot mean similarity of mapped objects to their unit codebook vector mapping <- predict(kohmap, classif=kohmap$classif) hits <- sort(unique(kohmap$classif)) similarities <- rep(NA, 25) for (i in seq(along=hits)) similarities[hits[i]] <- mean(mapping$distances[kohmap$classif == hits[i]]) plot(kohmap, type="property", property=similarities, main="property plot")