gwplot: Plot method for generalized weights
In neuralnet: Training of Neural Networks

Description Usage Arguments Author(s) References See Also Examples

gwplot, a method for objects of class nn, typically produced by neuralnet. Plots the generalized weights (Intrator and Intrator, 1993) for one specific covariate and one response variable.

1
2
3

gwplot(x, rep = NULL, max = NULL, min = NULL, file = NULL,
  selected.covariate = 1, selected.response = 1, highlight = FALSE,
  type = "p", col = "black", ...)

`x`	an object of class `nn`
`rep`	an integer indicating the repetition to plot. If rep="best", the repetition with the smallest error will be plotted. If not stated all repetitions will be plotted.
`max`	maximum of the y axis. In default, max is set to the highest y-value.
`min`	minimum of the y axis. In default, min is set to the smallest y-value.
`file`	a character string naming the plot to write to. If not stated, the plot will not be saved.
`selected.covariate`	either a string of the covariate's name or an integer of the ordered covariates, indicating the reference covariate in the generalized weights plot. Defaulting to the first covariate.
`selected.response`	either a string of the response variable's name or an integer of the ordered response variables, indicating the reference response in the generalized weights plot. Defaulting to the first response variable.
`highlight`	a logical value, indicating whether to highlight (red color) the best repetition (smallest error). Only reasonable if rep=NULL. Default is FALSE
`type`	a character indicating the type of plotting; actually any of the types as in `plot.default`.
`col`	a color of the generalized weights.
`...`	Arguments to be passed to methods, such as graphical parameters (see `par`).

Stefan Fritsch, Frauke Guenther guenther@leibniz-bips.de

Intrator O. and Intrator N. (1993) Using Neural Nets for Interpretation of Nonlinear Models. Proceedings of the Statistical Computing Section, 244-249 San Francisco: American Statistical Society (eds.)

neuralnet

data(infert, package="datasets")
print(net.infert <- neuralnet(case~parity+induced+spontaneous, infert, 
		                err.fct="ce", linear.output=FALSE, likelihood=TRUE))
gwplot(net.infert, selected.covariate="parity")
gwplot(net.infert, selected.covariate="induced")
gwplot(net.infert, selected.covariate="spontaneous")