predict.dannet: Predict New Examples by a Trained Discriminant Adaptive...
In schiffner/locClass: Collection of Local Classification Methods

Description Usage Arguments Details Value References See Also Examples

Predict new examples by a trained discriminant adaptive neural net.

1	predict.dannet(object, newdata, ...)

`object`	Object of class `"dannet"`.
`newdata`	A `matrix` or `data.frame` of test examples. A vector is considered to be a row vector comprising a single case.
`...`	Arguments passed to or from other methods.

This function is a method for the generic function predict() for class "dannet". It can be invoked by calling predict(x) for an object x of the appropriate class, or directly by calling predict.dannet(x) regardless of the class of the object.

In contrast to predict.nnet predict.dannet does not have a type argument. Since dannet is only suitabe for classification, both, the predicted posterior probabilities and the class labels, are returned.

A list with components:

`class`	The predicted class labels (a `factor`).
`posterior`	Matrix of class posterior probabilities.

Hand, D. J., Vinciotti, V. (2003), Local versus global models for classification problems: Fitting models where it matters, The American Statistician, 57(2) 124–130.

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

dannet, nnet.

1
2
3

fit <- dannet(Species ~ ., data = iris, wf = "gaussian", bw = 0.5, size = 2, rang = 0.1, decay = 5e-4, maxit = 200)
pred <- predict(fit)
mean(pred$class != iris$Species)