predict.nnet

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Predict New Examples by a Trained Neural Net

Predict new examples by a trained neural net.

Keywords
neural
Usage
"predict"(object, newdata, type = c("raw","class"), ...)
Arguments
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.
Details

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.

Value

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).

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.

See Also

nnet, which.is.max

Aliases
  • predict.nnet
Examples
library(nnet) # 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"))
Documentation reproduced from package nnet, version 7.3-12, License: GPL-2 | GPL-3

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