similar to: nnet 10-fold cross-validation

Dear colleague, Just an e-mail to know if they are people working in the field of chemoinformatic that are using R in their work. If yes I was wondering if we couldn't exchange tips and tricks about the use of R in this area ? Best regards Fred Ooms [[alternative HTML version deleted]]

I use a 32 bit machine. For those using 64 bit machines, what is the .Machine$double.xmin for for machines? regards. Stephen. ----- Original Message ----- From: Achim Zeileis <Achim.Zeileis at wu-wien.ac.at> Date: Tuesday, July 12, 2005 10:51 am Subject: Re: [R] exact values for p-values - more information. > On Tue, 12 Jul 2005, S.O. Nyangoma wrote: > > > > If they

Dear Sir/Madam I have recently started using the nnet package but cannot find any documentation other than the one page titled 'nnet {nnet}' which is replicated several times over the internet and is found in the help file for this package. I would like more information on how to use the package and have searched extensively over the internet but cannot find anything more. Do you know of

Dear All, I'm learning to train a neural network with my training data by using nnet package, then evaluate it with a evaluation set. My problem here is that, I need the trained network to be used in future, so, what should I store? and How? Any other options other than nnet package? Any example will be highly appreciated! Best, Baoqiang Cao

Dear R people, I want to use the tune.nnet function of e1071 package to tune nnet . I am unable to understand the parameters of tune.nnet from the e1071 pdf document. I have performed nnet on a traindata and want to test it for class prediction with a testdata. I want to know the values of size,decay,range etc. parameters for which the prediction of testdata is best. Can anyone please tell me

HI, Dear R community, My data set has 2409 variables, the last one is response variable. I have used the nnet after feature selection and works. But this time, I am using nnet to fit a model without feature selection. I got the following error information: > dim(train) [1] 1827 2409 nnet.fit<-nnet(as.factor(out) ~ ., data=train, size=3, rang=0.3, decay=5e-4, maxit=500) # model

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

Hi, I was trying to use the nnet library and am not sure of whats going on. I am calling the nnet function as: n <- nnet(x,y,size=3,subset=sets[[1]], maxit=200) Where x is a 272x4 matrix of observations (examples) and y is a 272x1 matrix of target values. However when I look at nnet$residuals they are off by two orders of magnitude (compared to the output from neural network code that I

DeaR useRs: I am looking for a function which fits a multinomial model and in Baron?s page I find the function "multinom" in package "nnet" but this package is deprecated. I suppose that this function is now in other package but I can't find it. Can you help me? Thanks.

I was wandering if anybody ever tried to compare the classification accuracy of nnet to other (rpart, tree, bagging) models. From what I know, there is no reason to expect a significant difference in classification accuracy between these models, yet in my particular case I get about 10% error rate for tree, rpart and bagging model and 80% error rate for nnet, applied to the same data. Thanks.

Hi, there: I am trying nnet as followed: > mg.nnet<-nnet(x=trn3[,r.v[1:100]], y=trn3[,209], size=5, decay = 5e-4, maxit = 200) # weights: 511 initial value 13822.108453 iter 10 value 7408.169201 iter 20 value 7362.201934 iter 30 value 7361.669408 iter 40 value 7361.294379 iter 50 value 7361.045190 final value 7361.038121 converged Error in y - tmp : non-numeric argument to binary operator

Hi R help, I run my data in nnet with skip layer, factor response (with 0 & 1 values) and explicitly put softmax=T to compare the result of the default nnet with no softmax specification. I assume this should give me the same result. I got the result the default one, but not the softmax version and I got the error message that I did not quite understand. test6.nn.skipT.softm.Yfac <-

Hello ! I'd like to know to which of the FANN package network corresponds the R nnet network ? In more details, what is the R nnet activation function, what is the training algorithm (rprop, quickprop, ...) ? Also, it seems that the R nnet "decay" parameter in nnet corresponds to the "learning_rate" parameter in FANN. Correct ? Many thanks in advance ! Luc Moulinier

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

> nnet.fit<-nnet(as.factor(out) ~ ., data=all_h, size=5, rang=0.3, decay=5e-4, maxit=500) # model fitting > summary(nnet.fit) a 23-5-1 network with 126 weights options were - entropy fitting decay=5e-04 HI, Guys, I can not find the manual to describe how the model is built, is there a more detailed description how nnet package works? -- Sincerely, Changbin -- [[alternative

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

I am using library(nnet) to train up an ANN with what I believe is a moderately sized dataset, but R is complaining about too many weights: --- > nn.1 <- nnet(t(data), targets, size = 4, rang = 0.1, decay = 5e-4, maxit = 200) Error in nnet.default(t(data), targets, size = 4, rang = 0.1, decay = 5e-04, : Too many (1614) weights > dim(targets) [1] 146 2 > dim(data) ## Note

Hi everyone, According to R reference manual, the nnet function uses the BFGS method of optim to optimize the neural network parameters. I would like, when calling the function nnet to tell the optim function not to produce the tracing information on the progress of the optimization, or at least to reduce the frequency of the reports. I tried the following: a) nnet default > x<-rnorm(20)

Hi, I am trying to determine the number of units in the hidden layer and the decay rate using the CVnn2 script found in MASS directory (reference: pg 348,MASS-4). The model that I am using is in the form of Y ~ X1 + X2 + X3... + X11 and the underlying data is time-series in nature. I found the MASS and nnet package extremely useful (many thanks to the contributors). However I am getting

I'm having some trouble with the nnet. Everytime I run it, it comes up with slightly different answers. I assume this is due to using a some sort of random seed to calculate the weights. Doesn't anyone have any examples of how to set these weights so that you can get consistent results? Thanks, Tom Paska