Hi Georg, The documentation (?nnet) says that y should be a matrix or data frame, but in your case it is a vector. This is most likely the problem, if you do not have other data issues going on. Convert y to a matrix (or data frame) using 'as.matrix' and see if this solves your problem. Library 'nnet' can do both classification and regression. I was able to replicate your problem, using an example from Modern Applied Statistics with S, Venables and Ripley, pages 246 and 247), by turning y into a vector and verifying that all the predicted values are the same when y is a vector. This is not the case when y is part of a data frame. You can see this by running the code below. I tried about 4 neural network packages in the past, including AMORE, but found 'nnet' to be the best for my needs.
Hope this helps. Jude # Neural Network model in Modern Applied Statistics with S, Venables and Ripley, pages 246 and 247 library(nnet) attach(rock) dim(rock) area1 <- area/10000; peri1 <- peri/10000 rock1 <- data.frame(perm, area = area1, peri = peri1, shape) dim(rock1) head(rock1,15) # skip = T rock.nn <- nnet(log(perm) ~ area + peri + shape, rock1, size=3, decay=1e-3, linout=T, skip=T, maxit=1000, Hess=T) rock1$actual <- log(perm) rock1$predicted <- predict(rock.nn) head(rock1,15) summary(rock.nn) sum((log(perm) - predict(rock.nn))^2) y <- as.vector(log(rock1$perm)) head(rock1[,c(2:4)]) test.nn <- nnet(x=rock1[,c(2:4)], y=y, size=3, linout=T, maxit=1000) head(predict(test.nn)) Georg wrote: Hi, I'm currently trying desperately to get the nnet function for training a neural network (with one hidden layer) to perform a regression task. So I run it like the following: trainednet <- nnet(x=traindata, y=trainresponse, size = 30, linout = TRUE, maxit=1000) (where x is a matrix and y a numerical vector consisting of the target values for one variable) To see whether the network learnt anything at all, I checked the network weights and those have definitely changed. However, when examining the trainednet$fitted.values, those are all the same so it rather looks as if the network is doing a classification. I can even set linout=FALSE and then it outputs "1" (the class?) for each training example. The trainednet$residuals are correct (difference between predicted/fitted example and actual response), but rather useless. The same happens if I run nnet with the formula/data.frame interface, btw. As per the suggestion in the ?nnet page: "If the response is not a factor, it is passed on unchanged to 'nnet.default'", I assume that the network is doing regression since my trainresponse variable is a numerical vector and _not_ a factor. I'm currently lost and I can't see that the AMORE/neuralnet packages are any better (moreover, they don't implement the formula/dataframe/predict things). I've read the manpages of nnet and predict.nnet a gazillion times, but I can't really find an answer there. I don't want to do classification, but regression. Thanks for any help. Georg. -- Research Assistant Otto-von-Guericke-Universit?t Magdeburg resea...@georgruss.de http://research.georgruss.de Jude Ryan MarketShare Partners 1270 Avenue of the Americas, Suite # 2702 New York, NY 10020 http://www.marketsharepartners.com Work: (646)-745-9916 ext: 222 Cell: (973)-943-2029 [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
