Diagnoistic and summary plots of a neural net regression object
Usage
plot.nnreg(out, model=out$best.model, main=NA, digits=4, graphics.reset=T, ...)
Arguments
out
|
A nnreg object
|
model
|
Model number to plot. Default is the best model based on GCV(2).
|
main
|
Title of the plot. Default is the function call.
|
digits
|
Number of significant digits for the RMSE label.
|
graphics.reset
|
Reset to original graphics parameters after function plotting.
|
...
|
Any plotting arguments.
|
Description
This function creates four summary plots of the tps object. The first
plot is a plot of the nnreg fit to the data. The second is predicted
values vs residuals. The third is number of parameters vs GCV with cost=1
and cost=2. The fourth plot is number of parameters vs root mean squared
error.See Also
nnreg, summary.nnregExamples
nnreg(ozone$x,ozone$y,1,2) -> fit # fitting a surface to ozone
# measurements, from 1 to 2 hidden units
plot(fit) # plots fit and residuals
nnreg(as.matrix(BD[,1:4]),BD[,5],1,5) -> fit # fitting DNA strand
# displacement amplification surface to various buffer compositions
plot(fit) # plots fit and residuals