nnet {nnet} | R Documentation |
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 class ~ x1 + x2 + ...
|
x |
matrix or data frame of x values for examples.
|
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 formula are
preferentially to be taken.
|
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 NA s are found.
The default action is for the procedure to fail. An alternative is
na.omit, which leads to rejection of cases with missing values on
any required variable. (NOTE: If given, this argument must be named.)
|
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. linout , entropy , softmax and censored are mutually
exclusive.
|
censored |
A variant on softmax , in which non-zero targets mean possible
classes. Thus for softmax a row of (0, 1, 1) means one example
each of classes 2 and 3, but for censored it means one example whose
class is only known to be 2 or 3.
|
skip |
switch to add skip-layer connections from input to output. |
rang |
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.
|
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 Hessian .
|
trace |
switch for tracing optimization. Default TRUE .
|
MaxNWts |
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 (and perhaps uninterruptable).
|
abstol |
Stop if the fit criterion falls below abstol , indicating an
essentially perfect fit.
|
reltol |
Stop if the optimizer is unable to reduce the fit criterion by a
factor of at least 1 - reltol .
|
... |
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. |
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.
data(iris3) # 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 = 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"))