initialize,SOMnn-method: Constructor of SOMnn Class

Description

The constructor creates a new object of type SOMnn.

Usage

# S4 method for SOMnn
initialize(
  .Object,
  name,
  codes,
  qerror,
  class.idx,
  classes,
  class.counts,
  class.freqs,
  confusion,
  measures,
  accuracy,
  xdim,
  ydim,
  len.total,
  toroidal,
  norm,
  norm.center,
  norm.scale,
  dist.fun,
  max.dist,
  strict
)

Arguments

.Object: SOMnn object
name: optional name of the model.
codes: data.frame with codebook vectors of the som.
qerror: sum of the mapping errors of the training data.
class.idx: numeric index of column with categories.
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).
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: Overall accuracy.
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).
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.
dist.fun: function; kernel for the kNN classifier.
max.dist: maximum distance \(\sigma\) 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).

Details

The constructor needs not to be called directly, because the normal way to create a SOMnn object is to use som.nn.train.