predict.nnet {nnet} | R Documentation |
Predict new examples by a trained neural net.
predict(object, newdata, type = c("raw","class"), ...)
object |
an object of class nnet as returned by nnet .
|
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
).
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"))