similar to: building a neural network on a unbalanced data set on R

Dear Sir or Madam, I've been using R for one year and I really appreciate it. I would like to know if a version performing parallel computations on multicore computers and computer clusters exists. Thank you very much. Yours sincerely. Enrico Giorgi _________________________________________________________________ [[alternative HTML version deleted]]

Dear all, I would like to plot n time series together. I've defined a matrix w, where each column contains one time series. With S-Plus usually I use the tsplot(.) command, but it seems that this command is not available in R. Can someone help me? Thank you, Enrico De Giorgi ************************************************ ETH Z?rich and University of Z?rich Enrico De Giorgi Researcher

Hi, I would like some help deciding if and how to average my data before running an ANOVA. Let me first describe the data and what makes it unique. Hopefully, that will generate some ideas because I am not sure what kind of model I need to use. I don't know how to describe this succinctly, so please bear with me. This is my last analysis for my dissertation and I really could use some help.

Hi, I have a experiment with block, plots, sub-plots, and sub-sub-plots with repeated measures and 3 factors (factorial design) when we have been observed diameter (mm), high (cm) and leaves number (count). However, we don't have one treatment in one factor, so, my design is unbalanced. On a previous message here, a friend tell me that "It appears to me that your design is a split-split

hi all , has anyone tried to predict a univariate time series by a neural networks packages ? please help me in this problem . I am new in R and I did not found any document that explains this problem. thanks in advance David [[alternative HTML version deleted]]

Joint COMPSAC/SAINT Doctoral Symposium : Call For Papers Munich, GERMANY (July 18-22, 2011) COMPSAC 2011 : The 35th Annual IEEE International Computer Software and Applications Conference http://conferences.computer.org/compsac/ SAINT2011 : The 11th Annual Annual International Symposium on Applications and

Joint COMPSAC/SAINT Doctoral Symposium : Call For Papers Munich, GERMANY (July 18-22, 2011) COMPSAC 2011 : The 35th Annual IEEE International Computer Software and Applications Conference http://conferences.computer.org/compsac/ SAINT2011 : The 11th Annual Annual International Symposium on Applications and

Dear all, I'm analysing a split-plot experiment, where there are sometimes one or two values missing. I realized that if the data is slightly unbalanced, the effect of the subplot-treatment will also appear and be tested against the mainplot-error term. I replicated this with the Oats dataset from Yates (1935), contained in the nlme package, where Variety is on mainplot, and nitro on

Dear all, I am trying to analyze data from an experiment like this: Factors: Hormone - Levels: SH, CH (S = without; C=with; H=Hormone) Time - Levels: 19/08/09, 04/09/09, 18/09/09, 08/10/09, 20/10/09 (DD/MM/YY) Nutrition - Levels: Completa, Sem (without) Macronutrition - Levels: Ca, K, Mg, P, Sem (without) Time is the measures day. It reflect the days after germination. Blocks : 4 plants per

This is a reminder. Please distribute this email. Data on both agile and plan-driven projects are welcome. To Whom It May Concern, My name is Donald Buresh, and I am a Ph.D. student at Northcentral University located in Prescott Valley, Arizona. The reason that I am writing to you is because I would like you to participate in an internet survey for my dissertation. The topic of my

Hi! I am studying to use some R libraries which are applied for working with artificial neural neworks (amore, nnet). Can you recommend some useful, reliable and easy to get example data to use in R for creating and testing a neural network? __________________________________________________________________ Make your browsing faster, safer, and easier with the new Internet

Hi, I am starting to play around with neural networks and noticed that there are several packages on the CRAN website for neural networks (AMORE, grnnR, neural, neuralnet, maybe more if I missed them). Are any of these packages more well-suited for newbies to neural networks? Are there any relative strengths / weaknesses to the different implementations? If anyone has any advice before I dive

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

I am interested in Neural network models in R. Is there any reference material/tutorial which i can use. Regards, ____________________________________________________________________________________ TV dinner still cooling? Check out "Tonight's Picks" on Yahoo! TV.

Hi, I am having difficulty in training a neural network using the package "neuralnet". My neural network has 2 input neurons (covariates), 1 hidden layer with 2 hidden neurons and 2 output neurons (responses). I am training my neural network with a dataset that has been transformed so that each column is of type "numeric". The difficulty I am facing is that the responses of

I am intersted in time-space clustering of local industry. I made the point file, polygons and time table then run 'stkhat' function in Splancs, but it generates only time k function. I noticed that it has only one ks value. ... Can anybody help me?. thanks in advance. countiespt <- read.table('d:/dissertation/dailynew/jun10/countiespt_ok.txt', sep=",")

Are there any packages in R that are suitable for doing time series forecasting using neural networks? I have looked in the nnet package and neural package and they both seem geared towards classification. thanks, Spencer [[alternative HTML version deleted]]

Hi folks, I want to apply a neural network to a data set to classify the observations in the different classes from a concrete response variable. The idea is to prove different models from network modifying the number of neurons of the hidden layer to control overfitting. But, to select the best model how I can choose the relevant variables? How I can eliminate those that are not significant for

Hi, I'm working on a classification problem and one of the methods I'd like to use are neural networks. I've been using nnet to build a classification network. However I would like to have the probabilities associated with the prediction. Are there any implementations of probabilistic neural networks available in R? thanks,

Hello! We are a group of three students at Bielefeld University currently working on a statistical projects about neural networks. Within the framework of this project we are supposed to use the nnet-function in R and explain how it works. Since anyone of us has much experience in using R we hoped to find some information on your homepage. Unfortunatelly, we haven't been very successfull so