nnet.RdFit 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, ...)A formula of the form class ~ x1 + x2 + ...
matrix or data frame of x values for examples.
matrix or data frame of target values for examples.
(case) weights for each example – if missing defaults to 1.
number of units in the hidden layer. Can be zero if there are skip-layer units.
Data frame from which variables specified in formula are
preferentially to be taken.
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
A function to specify the action to be taken if NAs 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.)
a list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
initial parameter vector. If missing chosen at random.
logical vector indicating which parameters should be optimized (default all).
switch for linear output units. Default logistic output units.
switch for entropy (= maximum conditional likelihood) fitting. Default by least-squares.
switch for softmax (log-linear model) and maximum conditional
likelihood fitting. linout, entropy, softmax and censored are mutually
exclusive.
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.
switch to add skip-layer connections from input to output.
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.
parameter for weight decay. Default 0.
maximum number of iterations. Default 100.
If true, the Hessian of the measure of fit at the best set of weights
found is returned as component Hessian.
switch for tracing optimization. Default TRUE.
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.
Stop if the fit criterion falls below abstol, indicating an
essentially perfect fit.
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.
# 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)
#> # weights: 19
#> initial value 56.349654
#> iter 10 value 49.382189
#> iter 20 value 21.951631
#> iter 30 value 18.895396
#> iter 40 value 18.624590
#> iter 50 value 18.416821
#> iter 60 value 18.142865
#> iter 70 value 18.059015
#> iter 80 value 17.940163
#> iter 90 value 17.923360
#> iter 100 value 17.919605
#> iter 110 value 17.912378
#> iter 120 value 17.910026
#> final value 17.909464
#> converged
test.cl <- function(true, pred) {
true <- max.col(true)
cres <- max.col(pred)
table(true, cres)
}
test.cl(targets[-samp,], predict(ir1, ir[-samp,]))
#> cres
#> true 1 2 3
#> 1 24 0 1
#> 2 0 25 0
#> 3 0 0 25
# 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)
#> # weights: 19
#> initial value 82.363571
#> iter 10 value 21.825347
#> iter 20 value 5.503315
#> iter 30 value 5.092956
#> iter 40 value 4.639916
#> iter 50 value 4.599500
#> iter 60 value 4.537793
#> iter 70 value 4.500333
#> iter 80 value 4.496499
#> iter 90 value 4.441225
#> iter 100 value 2.777553
#> iter 110 value 1.977550
#> iter 120 value 1.619453
#> iter 130 value 1.381772
#> iter 140 value 1.000324
#> iter 150 value 0.888485
#> iter 160 value 0.843000
#> iter 170 value 0.833129
#> iter 180 value 0.829150
#> iter 190 value 0.826909
#> iter 200 value 0.826441
#> final value 0.826441
#> stopped after 200 iterations
table(ird$species[-samp], predict(ir.nn2, ird[-samp,], type = "class"))
#>
#> c s v
#> c 24 0 1
#> s 0 25 0
#> v 2 0 23