| 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")