similar to: Issues with nnet.default for regression/classification

I want to create a neural network, and then everytime it receives new data, instead of creating a new nnet, i want to use a backpropagation algorithm to adjust the weights in the already created nn. I'm using nnet package, I know that nn$wts gives the weights, but I cant find out which weights belong to which conections so I could implement the backpropagation algorithm myself. But if anyone

Dear R-users, I have sent another mail some hour ago about a matlab Code I was trying to translate in R. Actually I have found a simpler code originally written in S-PLUS for the same function. Author's page -> http://math.bu.edu/people/murad/methods/locwhitt/ ============================================================= rfunc_function(h, len, im, peri) # h -- Starting H value for

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 ... $

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

I have a problem when using nnet to predict the negative values. For example : X = matrix(c(1,1,0,0,1,0,1,0),4,2) X Y = matrix(c(0,1,1,0)) - 0.5 # XOR - 0.5 Y nn = nnet(X,Y,size=3) val = predict(nn,X) val # this is expected to be close to Y, but it's not ! The 'val' is always positive. I tried to change the options, but the result isn't much better. Could someone give me an

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

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

Hi, I use nnet for my classification problem and have a problem concerning the calculation of the final value for my validation data.(nnet only calculates the final value for the training data). I made my own final value formula (for the training data I get the same value as nnet): # prob-matrix pmatrix <- cat*fittedValues tmp <- rowSums(pmatrix) # -log likelihood

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'm using nnet() for non-linear regression as in Ch8.10 of MASS. I understand that nnet() by default optimizes least squares. I'm looking to have it instead optimize such that the mean error is zero (so that it is unbiased). Any suggestions on how this might be achieved? Cheers, Mike -- Mike Lawrence http://artsweb.uwaterloo.ca/~m4lawren "The road to wisdom? Well,

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]]

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. 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

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

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

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

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 all, I am trying to write an application to monitor queues using the Asterisk Management API. So far I have had some level of sucess, basically reverse engineering the protocol and the event messages using ethereal etc. I know there are a couple of pages on the Wiki that attempt (no dis-respect to who ever did it as it has been a great help) to document the API and was wondering if there