Using nnet for prediction, am i doing it right?

I'm still pretty new to R and AI / ML techniques. I would like to use a neural net for prediction, and since I'm new I would just like to see if this is how it should be done.

As a test case, I'开发者_开发技巧m predicting values of sin(), based on 2 previous values. For training I create a data frame withy = sin(x), x1 = sin(x-1), x2 = sin(x-2), then use the formula y ~ x1 + x2.

It seems to work, but I am just wondering if this is the right way to do it, or if there is a more idiomatic way.

This is the code:

require(quantmod) #for Lag()
requre(nnet)
x <- seq(0, 20, 0.1)
y <- sin(x)
te <- data.frame(y, Lag(y), Lag(y,2))
names(te) <- c("y", "x1", "x2")
p <- nnet(y ~ x1 + x2, data=te, linout=TRUE, size=10)
ps <- predict(p, x1=y)
plot(y, type="l")
lines(ps, col=2)

Thanks

[edit]

Is this better for the predict call?

t2 <- data.frame(sin(x), Lag(sin(x)))
names(t2) <- c("x1", "x2")
vv <- predict(p, t2)
plot(vv)

I guess I'd like to see that the nnet is actually working by looking at its predictions (which should approximate a sin wave.)

---

I really like the caret package, as it provides a nice, unified interface to a variety of models, such as nnet. Furthermore, it automatically tunes hyperparameters (such as size and decay) using cross-validation or bootstrap re-sampling. The downside is that all this re-sampling takes some time.

#Load Packages
require(quantmod) #for Lag()
require(nnet)
require(caret)

#Make toy dataset
y <- sin(seq(0, 20, 0.1))
te <- data.frame(y, x1=Lag(y), x2=Lag(y,2))
names(te) <- c("y", "x1", "x2")

#Fit model
model <- train(y ~ x1 + x2, te, method='nnet', linout=TRUE, trace = FALSE,
                #Grid of tuning parameters to try:
                tuneGrid=expand.grid(.size=c(1,5,10),.decay=c(0,0.001,0.1))) 
ps <- predict(model, te)

#Examine results
model
plot(y)
lines(ps, col=2)

It also predicts on the proper scale, so you can directly compare results. If you are interested in neural networks, you should also take a look at the neuralnet and RSNNS packages. caret can currently tune nnet and neuralnet models, but does not yet have an interface for RSNNS.

/edit: caret now has an interface for RSNNS. It turns out if you email the package maintainer and ask that a model be added to caret he'll usually do it!

/edit: caret also now supports Bayesian regularization for feed-forward neural networks from the brnn package. Furthermore, caret now also makes it much easier to specify your own custom models, to interface with any neural network package you like!