similar to: maketitle garbles the title in package nnet (PR#613)

Dear All, I am playing with different models/packages (random forest, logistic regression, gbm etc...) for a problem of binomial regression (i.e. the outcome is 0/1, dead or alive etc...). I have used in the past the multinom function from the nnet library which uses the neural networks for multinomial regression. I wonder if I can safely use the same function to investigate a binary outcome. I

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 am trying to make a neural network learning a "noisy sine wave". Suppose I generate my data like so.. x <- seq(-2*pi, 2*pi, length=500) y <- sin(x) + rnorm(500, sd=sqrt(0.075)) I then train the neural net on the first 400 points using c <- nnet(as.matrix(x[1:400]),as.matrix(y[1:400]), size=3, maxit=10000, abstol=0.075, decay=0.007) Inspecting the fit of the training

Greetings list, I've just swapped from the "neural" package to the "nnet" package and I've noticed that the training is orders of magnitude faster, and the results are way more accurate. This leads me to wonder, what training algorithm is "nnet" using? Is it a modification on the standard backpropagation? Or a completely different algorithm? I'm

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 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

Hi, I just want to point out a discrepancy between the documentation of predict.nnet & the function definition. >?predict.nnet => predict.nnet package:nnet R Documentation Predict New Examples by a Trained Neural Net Description: Predict new examples by a trained neural net. Usage: predict.nnet(object, x,

Please, could you tell me how to enter a mixed or a purely conditional multinomial logit model in NNET. I know how to do a multinomial logit in NNET but I don't know how to do conditional or mixed models using this package. I do know how to do this with VGAM - but would like to compare my results to those obtained by NNET. Thanks. Raffaele.

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

good night Again I ask for help to the community, as I am new at this, I have some basic questions. I am looking for packages on neural networks and so you can search found these two that I think are the most used, neuralnet, nnet. So you can test, and correct me if I'm wrong the neuralnet only accepts as input values ??nomer, did a little test data (iris) library (neuralnet)

good night Again I ask for help to the community, as I am new at this, I have some basic questions. I am looking for packages on neural networks and so you can search found these two that I think are the most used, neuralnet, nnet. So you can test, and correct me if I'm wrong the neuralnet only accepts as input values ??nomer, did a little test data (iris) library (neuralnet)

Dear all, Objective: I am trying to learn about neural networks. I want to see if i can train an artificial neural network model to discriminate between spam and nonspam emails. Problem: I created my own model (example 1 below) and got an error of about 7.7%. I created the same model using the Rattle package (example 2 below, based on rattles log script) and got a much better error of about

Hello, I am pretty new to R. I am working on neural network classifiers and I am feeding the nnet input from different regions of interest (fMRI data). The script that I am using is this: library (MASS) heap_lda <- data.frame(as.matrix(t(read.table(file="R_10_5runs_matrix9.txt")))*100000,syll = c(rep("heap",3),rep("hoop",3),rep("hop",3))) library(nnet)

Hi, Am working on neural network. Below is the coding and the output > library (nnet) > uplift.nn<-nnet (PVU~ConsumerValue+Duration+PromoVolShare,y,size=3) # weights: 16 initial value 4068.052704 final value 3434.194253 converged > summary (uplift.nn) a 3-3-1 network with 16 weights options were - b->h1 i1->h1 i2->h1 i3->h1 16.64 6.62 149.93

Dearest all, Objective: I am now learning neural networks. I want to see how well can train an artificial neural network model to discriminate between the two files I am attaching with this message. http://r.789695.n4.nabble.com/file/n2240582/3dMaskDump.txt 3dMaskDump.txt http://r.789695.n4.nabble.com/file/n2240582/test_vowels.txt test_vowels.txt Question: when I am attempting to run

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.:

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

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, 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

Greetings, I am having trouble calculating artificial neural network misclassification errors using errorest() from the ipred package. I have had no problems estimating the values with randomForest() or svm(), but can't seem to get it to work with nnet(). I believe this is due to the output of the predict.nnet() function within cv.factor(). Below is a quick example of the problem I'm