plot-methods: Plot method for S4 class 'SOMnn'

In som.nn: Topological k-NN Classifier Based on Self-Organising Maps

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)

som.nn documentation built on May 29, 2024, 6:50 a.m.