plot.kohonen {kohonen}R Documentation

Plot kohonen object

Description

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

Usage

## S3 method for class 'kohonen':
plot(x, type = c("codes", "changes", "counts",
                 "dist.neighbours", "mapping", "property", "quality"),
     classif = NULL, labels = NULL, pchs = NULL, main = NULL,
     palette.name = heat.colors, ncolors, bgcol = NULL,
     zlim = NULL, heatkey = TRUE, property, contin,
     whatmap = NULL, codeRendering = NULL, keepMargins = FALSE,
     heatkeywidth = .2, ...) 

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", "property", and "quality" plotting types.
ncolors number of colors to use for the unit backgrounds. Default is 20 for continuous data, and the number of distinct values (if less than 20) for categorical data.
bgcol optional argument to colour the unit backgrounds for the "mapping" and "codes" plotting type. Defaults to "gray" and "transparent" in both types, respectively.
zlim optional range for color coding of unit backgrounds.
heatkey whether or not to generate a heatkey at the left side of the plot in the "property" and "counts" plotting types.
property values to use with the "property" plotting type.
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.
whatmap For supersom maps and a "codes" plot: what maps to show.
codeRendering How to show the codes. Possible choices: "segments", "stars" and "lines".
keepMargins if FALSE (the default), restore the original graphical parameters after plotting the kohonen map. If TRUE, one retains the map coordinate system so that one can add symbols to the plot, or map unit numbers using the identify function.
heatkeywidth width of the colour key; the default of 0.2 should work in most cases but in some cases, e.g. when plotting multiple figures, it may need to be adjusted.
... other graphical parameters, e.g. colours of labels, or plotting symbols, in the "mapping" plotting type.

Details

Several different types of plots are supported:

"changes"
shows the mean distance to the closest codebook vector during training.
"codes"
shows the codebook vectors.
"counts"
shows the number of objects mapped to the individual units. Empty units are depicted in gray.
"dist.neighbours"
shows the sum of the distances to all immediate neighbours. This kind of visualisation is also known as a U-matrix plot. Units near a class boundary can be expected to have higher average distances to their neighbours. Only available for the "som" and "supersom" maps, for the moment.
"mapping"
shows where objects are mapped. It needs the "classif" argument, and a "labels" or "pchs" argument.
"property"
properties of each unit can be calculated and shown in colour code. It can be used to visualise the similarity of one particular object to all units in the map, to show the mean similarity of all units and the objects mapped to them, etcetera. The parameter property contains the numerical values. See examples below.
"quality"
shows the mean distance of objects mapped to a unit to the codebook vector of that unit. The smaller the distances, the better the objects are represented by the codebook vectors.

Author(s)

Ron Wehrens

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")
plot(kohmap, type="codes", main = c("Codes X", "Codes Y"))
plot(kohmap, type="counts")
plot(kohmap, type="quality")
plot(kohmap, type="mapping", 
     labels=wine.classes, col=wine.classes+1,
     main="mapping plot")
xyfpredictions <- classmat2classvec(predict(kohmap)$unit.predictions)
bgcols <- c("gray", "pink", "lightgreen")
plot(kohmap, type="mapping", col=wine.classes+1,
     pchs=wine.classes, bgcol=bgcols[as.integer(xyfpredictions)], 
     main="another mapping plot")


[Package kohonen version 2.0.5 Index]