similar to: Need help on nnet

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 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 trying two combine two vectors that have different lengths. This without recursive the shorter one. E.g., a <- seq(1:3) b <- seq(1:6) Thanks in advance Serdar [[alternative HTML version deleted]]

Dear R-users, I've been reading a bunch of things on linear models but cannot quite find a clear answer. How can one determine whether a linear model is significant or not? For background info, I am modelling the response of topographic slope to the distance of a catchment's outlet. Some guys have shown that if there is a significant fit to a linear model, one can deduce the dynamic

Hello all, I've been comparing results from kmeans() in R to PROC FASTCLUS in SAS and I'm getting drastically different results with a real life data set. Even with a simulated data set starting with the same seeds with very well seperated clusters the resulting cluster means are still different. I was hoping to look at the source code of kmeans(), but it's in C and FORTRAN and

Dear all, I am looking for a clustering method usefull to classify the countries in some clusters taking account of: a) the geographical distance (in km) between countries and b) of some macroeconomic indicators (gdp, life expectancy...). Are there some packages in R usefull for this? Thanks a lot for your help, Dorina

Dear all, I am currently looking for a book about support vector machines for regression and classification and am a bit lost since they are plenty of books dealing with this subject. I am not totally new to the field and would like to get more information on that subject for later use with the e1071 <http://cran.r-project.org/web/packages/e1071/index.html> package for instance. Does

Hi all, I need some help with R. I am looking for a function that puts R back into a vanilla state (exactly the same when I just started it). Specifically I want all objects in the workspace removed and all non-base packages detached and unloaded; all base packages that are loaded on startup should remain loaded (and preferably a .Rprofile executed as well). It would also be good if all the

Hi there, this is more a comment and a solution rather than a question, but I thought I'd post it since it cost some time to dig down to the issue and maybe someone else could run into this. I'm using the nnet function for a regression task. I'm inputting the following data frame: > 'data.frame': 4970 obs. of 11 variables: $ EC25 : num 67.5 67.6 68 69 69.5 ... $

I have a data set with about 30,000 training cases and 103 variable. I've trained an SVM (using the e1071 package) for a binary classifier {0,1}. The accuracy isn't great. I used a grid search over the C and G parameters with an RBF kernel to find the best settings. I remember that for least squares, R has a nice stepwise function that will try combining subsets of variables to find

Hi, I'd like to download some data files from a remote server, the problem here is that some of the files actually don't exist, which I don't know before try. Just wondering if a function in R could tell me if a file exists on a remote server? I searched this mailing list and after read severals mails, still clueless. Any help will be highly appreciated. B.C.

For unsupervised approaches like BM25 this approach works well but letor does not need special weighting for title in this form as it itself assigns weights to title features separately. But I see your concern it would be a problem when BM25 is used on the index with this setup. Hence its preferable to take a note of this uplift in title weight for xapian-letor and normalize it everywhere

On 30.10.2013 12:19, Daniel Kiper wrote: > Hi, > multiboot2 protocol requires some more changes. However, about 80% of code > is ready. In this case Xen and modules are loaded by GRUB2 itself. It means > that all images could be placed on any filesystem recognized by GRUB2. Options > for Xen and modules are passed separately which simplifies command line editing > in boot loader

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

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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, if i have data in the following time series format: time, amount, state 1 2222 A 1 333 B 2 45 A 2 77 B where states could be n and time periods t is there a package in R that would calculate the transition probabilities in a markov chain. for each t except t=0 to generate A B A B perhaps the best structure might

Hello All, I would like to know what fit criterion (abstol arg) is in nnet. Is it the threshold for the difference btw the max output and target values? Is the value at each iteration also the difference btw max of output and target values over all output units (case of multiple classes)? How could value displayed at each iteration be related to SSE and abstol be related to threshold SSE,

Hi, I am trying to run a nnet algorithm but when I try to use the predict function with type='class', it gives the following error: fit <- nnet(y~., size = 1, data = train.set, rang = 0.5, maxit=200, decay = 0) predict<-predict(fit,test.set,type='class') Error in predict.nnet(fit, test.set, type = "class") : inappropriate fit for class I couldn't figure

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