# plot.kohonen

##### Plot kohonen object

Plot objects of class `kohonen`

. Several types
of plots are supported.

- Keywords
- classif

##### Usage

```
# S3 method for kohonen
plot(x, type = c("codes", "changes", "counts",
"dist.neighbours", "mapping", "property", "quality"),
whatmap = NULL, classif = NULL, labels = NULL,
pchs = NULL, main = NULL, palette.name = NULL,
ncolors, bgcol = NULL, zlim = NULL,
heatkey = TRUE, property, codeRendering = NULL,
keepMargins = FALSE, heatkeywidth = .2,
shape = c("round", "straight"), border = "black",
…)
# S3 method for kohonen
identify(x, …)
add.cluster.boundaries(x, clustering, lwd = 5, ...)
```

##### Arguments

- x
kohonen object.

- type
type of plot. (Wow!)

- whatmap
For a "codes" plot: what maps to show; for the "dist.neighbours" plot: what maps to take into account when calculating distances to neighbouring units.

- 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.

- 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.

- shape
kind shape to be drawn: "round" (circle) or "straight". Choosing "straight" produces a map of squares when the grid is "rectangular", and produces a map of hexagons when the grid is "hexagonal".

- border
color of the shape's border.

- lwd, …
other graphical parameters.

- clustering
cluster labels of the map units.

##### 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.

Function `identify.kohonen`

shows the number of a unit that is
clicked on with the mouse. The tolerance is calculated from the ratio
of the plotting region and the user coordinates, so clicking at any
place within a unit should work.

Function `add.cluster.boundaries`

will add to an existing plot of
a map thick lines, visualizing which units would be clustered
together. In toroidal maps, boundaries at the edges will only be shown
on the top and right sides to avoid double boundaries.

##### Value

Several types of plots return useful values (invisibly): the
`"counts"`

, `"dist.neighbours"`

, and `"quality"`

return
vectors corresponding to the information visualized in the plot (unit
background colours and heatkey).

##### See Also

##### Examples

```
# NOT RUN {
data(wines)
set.seed(7)
kohmap <- xyf(scale(wines), vintages,
grid = somgrid(5, 5, "hexagonal"), rlen=100)
plot(kohmap, type="changes")
counts <- plot(kohmap, type="counts", shape = "straight")
## show both sets of codebook vectors in the map
par(mfrow = c(1,2))
plot(kohmap, type="codes", main = c("Codes X", "Codes Y"))
par(mfrow = c(1,1))
similarities <- plot(kohmap, type="quality", palette.name = terrain.colors)
plot(kohmap, type="mapping",
labels = as.integer(vintages), col = as.integer(vintages),
main = "mapping plot")
## add background colors to units according to their predicted class labels
xyfpredictions <- classmat2classvec(getCodes(kohmap, 2))
bgcols <- c("gray", "pink", "lightgreen")
plot(kohmap, type="mapping", col = as.integer(vintages),
pchs = as.integer(vintages), bgcol = bgcols[as.integer(xyfpredictions)],
main = "another mapping plot", shape = "straight", border = NA)
## Show 'component planes'
set.seed(7)
sommap <- som(scale(wines), grid = somgrid(6, 4, "hexagonal"))
plot(sommap, type = "property", property = getCodes(sommap, 1)[,1],
main = colnames(getCodes(sommap, 1))[1])
## Show the U matrix
Umat <- plot(sommap, type="dist.neighbours", main = "SOM neighbour distances")
## use hierarchical clustering to cluster the codebook vectors
som.hc <- cutree(hclust(object.distances(sommap, "codes")), 5)
add.cluster.boundaries(sommap, som.hc)
## and the same for rectangular maps
set.seed(7)
sommap <- som(scale(wines),grid = somgrid(6, 4, "rectangular"))
plot(sommap, type="dist.neighbours", main = "SOM neighbour distances")
## use hierarchical clustering to cluster the codebook vectors
som.hc <- cutree(hclust(object.distances(sommap, "codes")), 5)
add.cluster.boundaries(sommap, som.hc)
# }
```

*Documentation reproduced from package kohonen, version 3.0.8, License: GPL (>= 2)*