R: Fit Neural Networks

nnet {nnet}

R Documentation

Fit Neural Networks

Description

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

Usage

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, ...)

Arguments

`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.

Details

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.

Value

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

References

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.

Examples

# 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"))

[Package nnet version 7.3-19 Index]