SOMnn-class {som.nn}

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).