nnetEval: Neural network evaluation by CV

In chemometrics: Multivariate Statistical Analysis in Chemometrics

Neural network evaluation by CV

Description

Evaluation for Artificial Neural Network (ANN) classification by cross-validation

Usage

nnetEval(X, grp, train, kfold = 10, decay = seq(0, 10, by = 1), size = 30, 
maxit = 100, plotit = TRUE, legend = TRUE, legpos = "bottomright", ...)

Arguments

X	standardized complete X data matrix (training and test data)
grp	factor with groups for complete data (training and test data)
train	row indices of X indicating training data objects
kfold	number of folds for cross-validation
decay	weight decay, see nnet, can be a vector with several values - but then "size" can be only one value
size	number of hidden units, see nnet, can be a vector with several values - but then "decay" can be only one value
maxit	maximal number of iterations for ANN, see nnet
plotit	if TRUE a plot will be generated
legend	if TRUE a legend will be added to the plot
legpos	positioning of the legend in the plot
...	additional plot arguments

Details

The data are split into a calibration and a test data set (provided by "train"). Within the calibration set "kfold"-fold CV is performed by applying the classification method to "kfold"-1 parts and evaluation for the last part. The misclassification error is then computed for the training data, for the CV test data (CV error) and for the test data.

Value

trainerr	training error rate
testerr	test error rate
cvMean	mean of CV errors
cvSe	standard error of CV errors
cverr	all errors from CV
decay	value(s) for weight decay, taken from input
size	value(s) for number of hidden units, taken from input

Author(s)

Peter Filzmoser <[e-mail]>

References

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

See Also

nnet

Examples

data(fgl,package="MASS")
grp=fgl$type
X=scale(fgl[,1:9])
k=length(unique(grp))
dat=data.frame(grp,X)
n=nrow(X)
ntrain=round(n*2/3)
require(nnet)
set.seed(123)
train=sample(1:n,ntrain)
resnnet=nnetEval(X,grp,train,decay=c(0,0.01,0.1,0.15,0.2,0.3,0.5,1),
   size=20,maxit=20)

chemometrics documentation built on Aug. 25, 2023, 5:18 p.m.