similar to: Logistical or Linear Output in AMORE

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

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

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

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

hi all i'm new to the area of neural networks. i've been reading some references and seem to understand some of the learning algorithms. i am very familiar with regression and would just like to see how neural nets handle this problem so i've been using the nnet package. i simply want to use a 3 layer neural net, ie 1 input, 1 hidden layer (where the hidden layer is linear, since i

Dear All, I am recently working on neural network using nnet package. The network has 4 hidden layers and 1 output layer, the target output 1 or 0. The model I use is as follows: nn<-nnet(target~f1+f2+f3+f4+f5+f6+f7+f8+f9+f10,data=train,size=4,linout=FALSE,decay=0.025,maxit=800) It works well and give me ROC 0.85. However, when I want to reproduce the result in java, I cannot get the same

Sorry for these multiple postings. I solved the problem using na.omit() to drop records with missing values for the time being. I will worry about imputation, etc. later. I calculated the sum of squared errors for 3 models, linear regression, neural networks, and support vector machines. This is the first run. Without doing any parameter tuning on the SVM or playing around with the number of

Hi All, I am trying to use a neural network for my work, but I am not sure about my approach to select a parsimonious model. In R with nnet, the IAC has not been defined for a feed-forward neural network with a single hidden layer. Is this because it does not make sens mathematically in this case? For example, is this pseudo code sensible? Thanks in advance for your help. I am sorry if this

Hi. I started with a file which was a sparse 982x923 matrix and where the last column was a variable to be predicted. I did principle component analysis on it and arrived at a new 982x923 matrix. Then I ran the code below to get a neural network using nnet and then wanted to get a confusion matrix or at least know how accurate the neural net was. I used the first 22 principle components only for

Hola compañeros de la lista. Los molesto con la siguiente duda. En un diseño central compuesto (CCD) con dos factores (V1 y V2) y una variable de respuesta (R), utilizando valores codificados (-1.4142, -1, 0, 1, 1.4182), al aplicar la orden: rsm.segundo.orden <- rsm(R ~ Bloque + SO(V1, V2), data = DATOS.Codificados) Obtengo el siguiente modelo: R = 103.92 -2.16

Hi all, I'm running a monte carlo test of a neural network tool I've developed, and it looks like it's going to take a very long time if I run it in R so I'm interested in translating my code (included below) into something faster like Fortran (which I'll have to learn from scratch). However, as you'll see my code loads the nnet library and uses it quite a bit, and I

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

Hello, I've been working with "nnet" and now I'd like to use the weigths, from the fitted model, to iterpret some of variables impornatce. I used the following command: mts <- nnet(y=Y,x=X,size =4, rang = 0.1, decay = 5e-4, maxit = 5000,linout=TRUE) X is (m x n) Y is (m x 1) And then I get the coeficients by: Wts<-coef(mts) b->h1 i1->h1

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

Dear List, I am trying to solve a problem by the neural network method(library: nnet). The problem is to express Weight in terms of Age , Sex and Height for twenty people. The data frame consists of 20 observations with four variables: Sex, Age, Height and Weight. Sex is treated as a factor, Age and Weight are variables normalized to unity, as usual. I wanted to construct a neural network, and so

Hi all I have a question about correct usage of persp(). I have a simple neural net-based XOR example, as follows: library(nnet) xor.data <- data.frame(cbind(expand.grid(c(0,1),c(0,1)), c(0,1,1,0))) names(xor.data) <- c("x","y","o") xor.nn <- nnet(o ~ x + y, data=xor.data, linout=FALSE, size=1) # Create an (x.y) surface and predict over all points d <-

Hello, I use nnet to do prediction for a continuous variable. after that, I calculate correlation coefficient between predicted value and real observation. I run my code(see following) several time, but I get different correlation coefficient each time. Anyone know why? In addition, How to calculate prediction accuracy for prediction of continuous variable? Aimin thanks, > m.nn.omega

I am running the nnet package as > neural.soft<-nnet(custcat~region+ed+marital+tenure+age+address+income,size=3,softmax=TRUE) This returns the error message : formal argument "softmax" matched by multiple actual arguments Here the dependent variable "custcat" is a factor with 4-levels. This error does not crop up for any other arguments of nnet(), including

Hi R-helpers, Can some one tell me how to train 'mynn' of this type?: mynn <- nnet(y ~ x1 + ..+ x8, data = lgist, size = 2, rang = 0.1, decay = 5e-4, maxit = 200) I assume that this nn is untrained, and to train I have to split the original data into train:test data set, do leave-one-out refitting to refine the weights (please straighten this up if I was wrong). I just don't know

Hi, I want to use the neural network package AMORE and I don't find in the documentation the weight decay option. Could someone tell if it is possible to add a regularization parameter (also known as a weight decay) to the training method. Is it possible to alter the gradient descent rule for that? Thanks, Ron