predict.osnnet: Predict New Examples Based on Observation Specific Neural...
In schiffner/locClass: Collection of Local Classification Methods

Description Usage Arguments Details Value See Also Examples

Predict new examples in conjunction with osnnet.

1	predict.osnnet(object, newdata, type = c("raw", "class"), ...)

`object`	Object of class `"osnnet"`.
`newdata`	A `data.frame` of cases to be classified or, if `object` has a `formula`, a `data.frame` with columns of the same names as the variables used. A vector will be interpreted as a row vector. If `newdata` is missing, an attempt will be made to retrieve the data used in `osnnet`.
`type`	Type of output.
`...`	Arguments passed to or from other methods.

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

If the response in formula is a factor, appropriate classification networks are constructed; these have one output and entropy fit if the number of levels is two, and a number of outputs equal to the number of classes and a softmax output stage for more levels. If the response is not a factor, it is passed on unchanged to predict.osnnet.

Optimization is done via the BFGS method of optim.

If type = "raw", the matrix of values returned by the trained network; if type = "class", the corresponding class (which is probably only useful if the net was generated by osnnet.formula).

osnnet, predict.nnet, nnet.

Other observation_specific nnet: osnnet

samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25))
fit <- osnnet(Species ~ ., data = iris, subset = samp, size = 2, 
               rang = 0.1, maxit = 200, bw = 0.5, reps = 2)
pred <- predict(fit)