Fit Neural Networks
Fit single-hidden-layer neural network, possibly with skip-layer connections.
nnet(x, ...)
## S3 method for class 'formula'
nnet(formula, data, weights, ...,
subset, na.action, contrasts = NULL)
## Default S3 method:
nnet(x, y, weights, size, Wts, mask,
linout = FALSE, entropy = FALSE, softmax = FALSE,
censored = FALSE, skip = FALSE, rang = 0.7, decay = 0,
maxit = 100, Hess = FALSE, trace = TRUE, MaxNWts = 1000,
abstol = 1.0e-4, reltol = 1.0e-8, ...)formula |
A formula of the form |
x |
matrix or data frame of |
y |
matrix or data frame of target values for examples. |
weights |
(case) weights for each example – if missing defaults to 1. |
size |
number of units in the hidden layer. Can be zero if there are skip-layer units. |
data |
Data frame from which variables specified in |
subset |
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) |
na.action |
A function to specify the action to be taken if |
contrasts |
a list of contrasts to be used for some or all of the factors appearing as variables in the model formula. |
Wts |
initial parameter vector. If missing chosen at random. |
mask |
logical vector indicating which parameters should be optimized (default all). |
linout |
switch for linear output units. Default logistic output units. |
entropy |
switch for entropy (= maximum conditional likelihood) fitting. Default by least-squares. |
softmax |
switch for softmax (log-linear model) and maximum conditional
likelihood fitting. |
censored |
A variant on |
skip |
switch to add skip-layer connections from input to output. |
rang |
Initial random weights on [- |
decay |
parameter for weight decay. Default 0. |
maxit |
maximum number of iterations. Default 100. |
Hess |
If true, the Hessian of the measure of fit at the best set of weights
found is returned as component |
trace |
switch for tracing optimization. Default |
MaxNWts |
The maximum allowable number of weights. There is no intrinsic limit
in the code, but increasing |
abstol |
Stop if the fit criterion falls below |
reltol |
Stop if the optimizer is unable to reduce the fit criterion by a
factor of at least |
... |
arguments passed to or from other methods. |
If the response in formula is a factor, an appropriate classification
network is constructed; this has 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 nnet.default.
Optimization is done via the BFGS method of optim.
object of class "nnet" or "nnet.formula".
Mostly internal structure, but has components
wts |
the best set of weights found |
value |
value of fitting criterion plus weight decay term. |
fitted.values |
the fitted values for the training data. |
residuals |
the residuals for the training data. |
convergence |
|
Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
# use half the iris data
ir <- rbind(iris3[,,1],iris3[,,2],iris3[,,3])
targets <- class.ind( c(rep("s", 50), rep("c", 50), rep("v", 50)) )
samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25))
ir1 <- nnet(ir[samp,], targets[samp,], size = 2, rang = 0.1,
decay = 5e-4, maxit = 200)
test.cl <- function(true, pred) {
true <- max.col(true)
cres <- max.col(pred)
table(true, cres)
}
test.cl(targets[-samp,], predict(ir1, ir[-samp,]))
# or
ird <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
species = factor(c(rep("s",50), rep("c", 50), rep("v", 50))))
ir.nn2 <- nnet(species ~ ., data = ird, subset = samp, size = 2, rang = 0.1,
decay = 5e-4, maxit = 200)
table(ird$species[-samp], predict(ir.nn2, ird[-samp,], type = "class"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.