predict.nnet(object, newdata, type=c("raw","class"), ...)
nnet
as returned by
nnet
.
type="raw"
, the matrix of values returned by the trained network;
type="class"
, the corresponding class (which is probably only
useful if the net was generated by
nnet.formula
).
This function is a method for the generic function for class nnet. It can be invoked by calling for an object x of the appropriate class, or directly by calling regardless of the class of the object.
# use half the iris data ir <- rbind(iris[,,1],iris[,,2],iris[,,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(iris[,,1], iris[,,2], iris[,,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"))