similar to: nnet classification using unbalanced classes

Hi I got two questions about the nnet function in R. I would be thankful to have an answer. 1) Does the function intrinsically normalize the X and Y matrices before the training, or normalization should be done by the user. 2) I need to understand the $wts matrix. I do imagine that it is a single column transformation of the two matrices of weighs (input to hidden Ninputs+1 x Nodes) and

Hi everyone, I am trying to build a R library called nnNorm but I have some troubles checking and installing it. Here is the setup: If I don't use a inst\doc directory(with the vignettes files) in my source, the install step is working fine: C:\test>RCMD INSTALL nnNorm ---------- Making package nnNorm ------------ adding build stamp to DESCRIPTION installing R files installing

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'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 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.

I hope this question is adequate for this list I use the nnet code from V&R p. 348: The very nice and general function CVnn2() to choose the number of hidden units and the amount of weight decay by an inner cross-validation- with a slight modification to use it for classification (see below). My data has 2 classes with unequal size: 45 observations for classI and 116 obs. for classII With

Hi all, I am trying to run R CMD check on a package which passes R CMD INSTALL. The check stops because of a parsing problem in the example of a given function at this line: return(res[res$ID %in% list$targetGeneSets,]) The code is ok, since it runs if I paste it in R. Is this a known parsing issue in R CMD check? Thanks, Adi > sessionInfo() R version 2.13.0 (2011-04-13) Platform:

Hi all, I have a dataset in which the output Y is observed on two groups of patients (treatment factor T with 2 levels). Every subject in each group is observed three times (not time points but just technical replication). I am interested in estimating the treatment effect and take into account the fact that I have repeated measurements for every subject. If I do this with repeated measures

Dear R people, I am using tune.nnet from e1071 package to tune the parameters for nnet. I am using the following syntax: tuneclass <-c(rep(1,46),rep(2,15)) tunennet <-tune.nnet(x=traindata,y=tuneclass,size=c(50,75,100),decay=c(0,0.005,0.010),MaxNWts = 20000) Here traindata is the training data that I want to tune for nnet which is a matrix with 61 rows(samples) and 200

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,

One possibility is to look at predict.nnet(), and - Write an R function that write out parts of an nnet object that are needed by predict.nnet() to an external file. - Re-write predict.nnet() in C, reading the model information from the external file. Obviously you'll also need the C source for the code that predict.nnet() calls, and modify those as needed to strip out dependency on R, if

Hello All, I am working with samr and nnet packages. I am following the steps given below: 1> I take a input file with signal values for 9506 genes and 36 chips , belonging to two classes. 2> I perform samr analysis on 80% of chip data from both the classes.(selected by random sampling) 3> I then use the data of only the significant genes from this samr analysis to train nnet. 4>

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

I've tried with KSM disabled and nothing changed. I've upgraded KVM to qemu-kvm-ev. I'm waiting to see if there are any improvements and report back. ?n mie., 17 aug. 2016 la 15:10, Boris Derzhavets <bderzhavets at hotmail.com> a scris: > For myself KSM is unpredictable feature. The problem is Compute, just this > node > > does "copy on write" , so only

For myself KSM is unpredictable feature. The problem is Compute, just this node does "copy on write" , so only Compute. My concern exactly is where would it lead to worse or better Guest behavior ? I am not expecting complete fix. I would track via top/htop and dmesg via Cron on 1-2 hr period. ________________________________ From: centos-virt-bounces at centos.org

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

Both baremetal and compute ? Are there any other metrics do you consider useful to collect for troubleshooting purposes ? ?n mie., 17 aug. 2016 la 13:04, Boris Derzhavets <bderzhavets at hotmail.com> a scris: > It sounds weird, but attempt to disable KSM and see would it help or no ? > > > ------------------------------ > *From:* centos-virt-bounces at centos.org

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

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)