| predict.nnet {nnet} | R Documentation |
Predict new examples by a trained neural net.
## S3 method for class 'nnet'
predict(object, newdata, type = c("raw","class"), ...)
object |
an object of class |
newdata |
matrix or data frame of test examples. A vector is considered to be a row vector comprising a single case. |
type |
Type of output |
... |
arguments passed to or from other methods. |
This function is a method for the generic function
predict() for class "nnet".
It can be invoked by calling predict(x) for an
object x of the appropriate class, or directly by
calling predict.nnet(x) regardless of the
class of the object.
If type = "raw", the matrix of values returned by the trained network;
if type = "class", the corresponding class (which is probably only
useful if the net was generated by nnet.formula).
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"))