similar to: A simple perceptron neural network (nnet)

Hi all, I have considered neural network to classify the health status of the cow. I found a very neatly written R codes for classification method in here<http://home.strw.leidenuniv.nl/~jarle/IAC/RRoutines/classification-example.R> . It would be very helpful if you can answer some of the questions, that I am struggling with, I have set of time series data from different animals, I use

Hi, I have a binary variable and many explanatory variables and I want to use the package "nnet" to model these data, (instead of logistic regression). I want to find the more effective variables (inputs to the network) in the neural network model. how can I do this? thanks. [[alternative HTML version deleted]]

HI, Dear R community, I am using the nnet to fit a neural network model to do classification on binary target variable (0, 1). I am using the following codes: nnet.fit<-nnet(as.factor(out) ~ ., data=train, size=5, rang=0.3, decay=5e-4, maxit=500) I want to know what is the activation function for the original inputs, is it sigmoid activation function? and what is the output activation

Hello there, I'm back again with another question about the neural network package. I'm having trouble getting the network to run for the maximum number of iterations. It always stops early, usually after 100 iterations claiming to have converged at an answer. Now, for my purposes I want it to run for the entire number of epochs, and I'm been looking at modifying the abstol

Hi all, I am trying to develop a neural network(Multilayer perceptron) with the package 'NEURALNET'. I have some doubts on it, 1. Whether this procedure taken care about categorical input variables- The reason is I could not find any option to describe type of variable in the arguments? 2.The algorithm is providing input variables generalized weights like this, >

I am new to R and use nnetTs - calls. If a time series of let's say 80000 Datapoints and did call nnetTs I want make a new net for the old ponts plus the next 1000 points (81000 datapoints total) what would again cost much calculation time. So I want to pre-init the new net with the former wonnen net to reduce the necessary iteration numbers. Is thee a possibility to do that and how? i.e.:

Hy, I try to give an example of overfitting with multi-layer perceptron. I have done following small example : library(nnet) set.seed(1) x <- matrix(rnorm(20),10,2) z <- matrix(rnorm(10),10,1) rx <- max(x)-min(x) rz <- max(z)-min(z) x <- x/rx z <- z/rz erreur <- 10^9 for(i in 1:100){ temp.mod <- nnet(x=x,y=z,size=10,rang=1,maxit=1000) if(temp.mod$value<erreur){

Hi all, I am trying to develop a neural network with single target variable and 5 input variables to predict the importance of input variables using R. I used the packages nnet and RSNNS. But unfortunately I could not interpret the out put properly and the documentation of that packages also not giving proper direction. Please help me to find a good package with a proper documentation for neural

Hi, I'm using the R nnet package to train a classifier to recognise items which belong to a particular class and those which don't belong to the class. I'm supplying nnet with a matrix x containing training examples (in each row) and a matrix y of targets. The training set is made up of 200 positive examples and 1000 negative examples. I want to train the network on the same

Hello R-Users, I have been using (nnet) by Ripley to train a neural net on a test dataset, I have obtained predictions for a validtion dataset using: PP<-predict(nnetobject,validationdata) Using PP I can find the -2 log likelihood for the validation datset. However what I really want to know is how well my nueral net is doing at classifying my binary output variable. I am new to R and I

Hi all, I am working for my master's thesis and I need to do a neural network to forecast stock market price, with also external inputs like technical indicators. I would like to know which function and package of R are more suitable for this study. Thanks a lot for your response, Arnaud TREBAOL. -- Arnaud Trébaol T.I.M.E. Student Ecole Centrale de Lille (09) Politecnico di Milano (10)

I am exploring neural networks (adding non-linearities) to see if I can get more predictive power than a linear regression model I built. I am using the function nnet and following the example of Venables and Ripley, in Modern Applied Statistics with S, on pages 246 to 249. I have standardized variables (z-scores) such as assets, age and tenure. I have other variables that are binary (0 or 1). In

Hi All, I am trying to learn Neural Networks. I found that R has packages which can help build Neural Nets - the popular one being AMORE package. Is there any book / resource available which guides us in this subject using the AMORE package? Any help will be much appreciated. Thanks, Indrajit

I am using the iris example came with nnet package to test AMORE. I can see the outcomes are similar to nnet with adaptative gradient descent. However, when I changed the method in the newff to the batch gradient descent, even by setting the epoch numbers very large, I still found all the iris expected class=2 being classified as class=3. In addition, all those records in the outcomes (y) are the

As a follow-up to my email below: The input data frame to nnet() has dimensions: > dim(coreaff.trn.nn) [1] 5088 8 And the predictions from the neural network (35 records are dropped - see email below for more details) has dimensions: > pred <- predict(coreaff.nn1) > dim(pred) [1] 5053 1 So, the following line of R code does not work as the dimensions are

Hello! We are a group of three students at Bielefeld University currently working on a statistical projects about neural networks. Within the framework of this project we are supposed to use the nnet-function in R and explain how it works. Since anyone of us has much experience in using R we hoped to find some information on your homepage. Unfortunatelly, we haven't been very successfull so

Hi, I am having difficulty in training a neural network using the package "neuralnet". My neural network has 2 input neurons (covariates), 1 hidden layer with 2 hidden neurons and 2 output neurons (responses). I am training my neural network with a dataset that has been transformed so that each column is of type "numeric". The difficulty I am facing is that the responses of

Hello all, Ive been playing with nnet (package 'nnet') and Ive come across this problem. nnet doesnt seems to like to have more than 1000 weights. If I do: > data(iris) > names(iris)[5] <- "species" > net <- nnet(species ~ ., data=iris, size=124, maxit=10) # weights: 995 initial value 309.342009 iter 10 value 21.668435 final value 21.668435 stopped after 10

Hello everybody, I work in a production plant as an operations analyst. I have been using R for two years, starting with my final dissertation project at college. We have the following problem in our plant. At the end of the production process, each joint (that is what we produce) must pass a final electrical test. The result can be 0 or 1. We think that this may depend on some raw

Hi! I am studying to use some R libraries which are applied for working with artificial neural neworks (amore, nnet). Can you recommend some useful, reliable and easy to get example data to use in R for creating and testing a neural network? __________________________________________________________________ Make your browsing faster, safer, and easier with the new Internet