R: Plot method for S4 class 'SOMnn'

plot,SOMnn,ANY-method {som.nn}

R Documentation

Plot method for S4 class `SOMnn`

Description

Creates a plot of the hexagonal som in the model of type SOMnn.

Usage

## S4 method for signature 'SOMnn,ANY'
plot(
  x,
  title = TRUE,
  col = NA,
  onlyDefCols = FALSE,
  edit.cols = FALSE,
  show.legend = TRUE,
  legend.loc = "bottomright",
  legend.width = 4,
  window.width = NA,
  window.height = NA,
  show.box = TRUE,
  show.counter.border = 0.98,
  predict = NULL,
  add = FALSE,
  pch.col = "black",
  pch = 19,
  ...
)

Arguments

`x`	trained som of type `SOMnn`.
`title`	`logical`; if TRUE, slots name and date are used as main title.
`col`	defines colours for the classes of the dataset. Possible values include: `NA`: default value; colours are generated with `rainbow`, a `vector` of colour definitions or a `data.frame` with categories in the first and respective colours in the second column.
`onlyDefCols`	`logical`; if TRUE, only categories are plotted, for which colours are defined. Default: FALSE.
`edit.cols`	`logical`; if TRUE, colour definitions can be edited interactively before plotting. Default: FALSE.
`show.legend`	`logical`; if TRUE, a legend is displayed,. Default: TRUE.
`legend.loc`	Legend position as specified for `legend`. Default is `"bottomright"`.
`legend.width`	size of the legend.
`window.width`	Manual setting of window width. Default is NA.
`window.height`	Manual setting of window height. Default is NA.
`show.box`	Show frame around the plot . Default is TRUE.
`show.counter.border`	Percentile as limit for the display of labels in the pie charts. Default is 0.98. Higher counts are displayed as numbers in the neuron.
`predict`	`data.frame` as returned by the `som.nn::predict` function or a `data.frame` or matrix that follows the specification: If columns `x` and `y` exist, these are used as coordinates for the traget neuron; otherwise the first two columns are used. Default: NULL.
`add`	`logical`; if TRUE, points are plotted on an existing plot. This can be used to stepwise plot points of different classes with different colours or symbols.
`pch.col`	Colour of the markers for predicted samples.
`pch`	Symbol of the markers for predicted samples.
`...`	More parameters as well as general plot parameters are allowed; see `par`.

Details

In addition to the required parameters, many options can be specified to plot predicted samples and to modify colours, legend and scaling.

Examples

## get example data and add class labels:
data(iris)
species <- iris$Species

## train with default radius = diagonal / 2:
rlen <- 500
som <- som.nn.train(iris, class.col = "Species", kernel = "internal",
                    xdim = 15, ydim = 9, alpha = 0.2, len = rlen, 
                    norm = TRUE, toroidal = FALSE)


## continue training with different alpha and radius;
som <- som.nn.continue(som, iris, alpha = 0.02, len=500, radius = 5)
som <- som.nn.continue(som, iris, alpha = 0.02, len=500, radius = 2)

## predict some samples:
unk <- iris[,!(names(iris) %in% "Species")]

setosa <- unk[species=="setosa",]
setosa <- setosa[sample(nrow(setosa), 20),]

versicolor <- unk[species=="versicolor",]
versicolor <- versicolor[sample(nrow(versicolor), 20),]

virginica <- unk[species=="virginica",]
virginica <- virginica[sample(nrow(virginica), 20),]

p <- predict(som, unk)
head(p)

## plot:
plot(som)
dev.off()
plot(som, predict = predict(som, setosa))
plot(som, predict = predict(som, versicolor), add = TRUE, pch.col = "magenta", pch = 17)
plot(som, predict = predict(som, virginica), add = TRUE, pch.col = "white", pch = 8)