SOMnn-class: An S4 class to hold a model for the topological classifier...
In som.nn: Topological k-NN Classifier Based on Self-Organising Maps

SOMnn-class

R Documentation

An S4 class to hold a model for the topological classifier som.nn

Description

Objects of type SOMnn can be created by training a self-organising map with som.nn.train.

Slots

name: optional name of the model.
date: time and date of creation.
codes: data.frame with codebook vectors of the som.
qerror: sum of the mapping errors of the training data.
class.idx: column index of column with class labels in input data.
classes: character vector with names of categories.
class.counts: data.frame with class hits for each neuron.
class.freqs: data.frame with class frequencies for each neuron (freqs sum up to 1).
norm: logical; if TRUE, data is normalised before training and mapping. Parameters for normalisation of training data is stored in the model and applied before mapping of test data.
norm.center: vector of centers for each column of training data.
norm.scale: vector of scale factors for each column of training data.
confusion: data.frame with confusion matrix for training data.
measures: data.frame with classes as rows and the columns sensitivity, specificity and accuracy for each class.
accuracy: The overall accuracy calculated based on the confusion matrix cmat: acc = sum(diag(cmat)) / sum(cmat).
xdim: number of neurons in x-direction of the som.
ydim: number of neurons in y-direction of the som.
len.total: total number of training steps, performed to create the model.
toroidal: logical; if TRUE, the map is toroidal (i.e. borderless).
dist.fun: function; kernel for the kNN classifier.
max.dist: maximum distance for the kNN classifier.
strict: Minimum vote for the winner (if the winner's vote is smaller than strict, "unknown" is reported as class label (default = 0.8).