`linout=TRUE` is hardcoded for regression. `size` has been set to `3` by default.

Argument of: nnet::nnet

number of units in the hidden layer. Can be zero if there are skip-layer units.

Argument of: nnet::nnet

switch to add skip-layer connections from input to output.

Argument of: nnet::nnet

Initial random weights on [-rang, rang].  Value about 0.5 unless the inputs are large, in which case it should be chosen so that rang * max(|x|) is about 1.

Argument of: nnet::nnet

parameter for weight decay.  Default 0.

Argument of: nnet::nnet

maximum number of iterations. Default 100.

Argument of: nnet::nnet

If true, the Hessian of the measure of fit at the best set of weights found is returned as component Hessian.

Argument of: nnet::nnet

switch for tracing optimization. Default TRUE.

Argument of: nnet::nnet

The maximum allowable number of weights.  There is no intrinsic limit in the code, but increasing MaxNWts will probably allow fits that are very slow and time-consuming.

Argument of: nnet::nnet

Stop if the fit criterion falls below abstol, indicating an essentially perfect fit.

Argument of: nnet::nnet

Stop if the optimizer is unable to reduce the fit criterion by a factor of at least 1 - reltol.

Defined as: mean(y), where y is the vector of costs for the predicted classes.