Prepares graphical display of new data fitted by a neural
net that was modeled on the training data, using the output
of vcr.neural.train on the training data.
vcr.neural.newdata(Xnew, ynew = NULL, probs,
vcr.neural.train.out)A list with components:
number of the given class of each case. Can contain NA's.
given class label of each case. Can contain NA's.
levels of the response, from vcr.svm.train.out.
predicted class number of each case. Always exists.
predicted label of each case.
number of the alternative class. Among the classes different from the given class, it is the one with the highest posterior probability. Is NA for cases whose ynew is missing.
alternative label if yintnew was given, else NA.
probability of the alternative class. Is NA for cases whose ynew is missing.
distance of each case \(i\) from each class \(g\). Always exists.
farness of each case from its given class. Is NA for cases whose ynew is missing.
for each case \(i\), its lowest fig[i,g] to any class \(g\). Always exists.
data matrix of the new data, with the same number of columns as in the training data. Missing values in Xnew are not allowed.
factor with class membership of each new case. Can be NA for some or all cases. If NULL, is assumed to be NA everywhere.
posterior probabilities obtained by running the neural net on the new data.
output of vcr.neural.train on the training data.
Raymaekers J., Rousseeuw P.J.
Raymaekers J., Rousseeuw P.J.(2021). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. (link to open access pdf)
vcr.neural.train, classmap, silplot, stackedplot
# For examples, we refer to the vignette:
if (FALSE) {
vignette("Neural_net_examples")
}
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