Fit single-hidden-layer neural network, possibly with skip-layer connections.

`nnet(x, …)`# S3 method for formula
nnet(formula, data, weights, …,
subset, na.action, contrasts = NULL)

# S3 method for default
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.

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.

object of class `"nnet"`

or `"nnet.formula"`

.
Mostly internal structure, but has components

the best set of weights found

value of fitting criterion plus weight decay term.

the fitted values for the training data.

the residuals for the training data.

`1`

if the maximum number of iterations was reached, otherwise `0`

.

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`

.

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.

# NOT RUN { # 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")) # }

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