similar to: generalized regression neural nets

I checked GlmmML package. However, it can only do binomial and poisson distribution. How about others such as gamma or neg binomial? Thank you so much! wensui

Hi everyone, I'm a graduate student of engineering, lately introduced with R. and using R for my project and thesis. I'm trying to use R for implementing a neural network regression model and apply it to my database. I found three R packages ("AMORE" , "grnnR" , "neural") in R website, but their manuals are not really user-friendly in my idea. I was wondering

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

Hello R-Users, I have been using (nnet) by Ripley to train a neural net on a test dataset, I have obtained predictions for a validtion dataset using: PP<-predict(nnetobject,validationdata) Using PP I can find the -2 log likelihood for the validation datset. However what I really want to know is how well my nueral net is doing at classifying my binary output variable. I am new to R and I

Hi All, I am trying to learn Neural Networks. I found that R has packages which can help build Neural Nets - the popular one being AMORE package. Is there any book / resource available which guides us in this subject using the AMORE package? Any help will be much appreciated. Thanks, Indrajit

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.

Hello list, I've been searching around trying to find whether somebody has written such a package of least angle regression on generalized linear models, like what Lasso2 package does. The extension to generalized linear models is briefly discussed in the comment by D. Madigan and G. Ridgeway. Is such a package available? Thanks, Mike [[alternative HTML version deleted]]

Dear R community, I have a dataframe with say 100 different variables. I wish to regress variable 1 separately on every other variable (2-100) in a linear regression using lm. There must be an easy way to do this without loops, but I have difficulties figuring this out... Can you please help? Thank you and best regards, Georg. ***************************************** Georg Ehret Johns Hopkins

I remember in one slide of Prof. Ripley's presentation overhead, he said the most popular data analysis software is excel. So is there any resource or tutorial on this topic? Thank you so much!

Hi all, I am trying to develop a neural network(Multilayer perceptron) with the package 'NEURALNET'. I have some doubts on it, 1. Whether this procedure taken care about categorical input variables- The reason is I could not find any option to describe type of variable in the arguments? 2.The algorithm is providing input variables generalized weights like this, >

Right now, I am using SAS and S+/R. As a new year resolution, I am planning to learn a scripting language. from statisticians' point of view, which scripting language is worth to learn, perl, python, or any other recommendation? (Most likely, I will be learning it in windows.) Since I am not in research, I will prefer one widely used in industry and related to statistical work. if you

I am exploring neural networks (adding non-linearities) to see if I can get more predictive power than a linear regression model I built. I am using the function nnet and following the example of Venables and Ripley, in Modern Applied Statistics with S, on pages 246 to 249. I have standardized variables (z-scores) such as assets, age and tenure. I have other variables that are binary (0 or 1). In

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]]

As a follow-up to my email below: The input data frame to nnet() has dimensions: > dim(coreaff.trn.nn) [1] 5088 8 And the predictions from the neural network (35 records are dropped - see email below for more details) has dimensions: > pred <- predict(coreaff.nn1) > dim(pred) [1] 5053 1 So, the following line of R code does not work as the dimensions are

Hi, I have a binary variable and many explanatory variables and I want to use the package "nnet" to model these data, (instead of logistic regression). I want to find the more effective variables (inputs to the network) in the neural network model. how can I do this? thanks. [[alternative HTML version deleted]]

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

Sorry for these multiple postings. I solved the problem using na.omit() to drop records with missing values for the time being. I will worry about imputation, etc. later. I calculated the sum of squared errors for 3 models, linear regression, neural networks, and support vector machines. This is the first run. Without doing any parameter tuning on the SVM or playing around with the number of

Dear sir, I want to know about R command for parameter estimation for generalized Poisson regression. yours faithfully Arafat Ud Zaman 4th year(Hons) Applied statistics University of Dhaka Mob- +8801715173176 E-mail: arafat.zaman@yahoo.com azaman1@isrt.ac.bd azaman1@quantummethod.org.bd [[alternative HTML version deleted]]

Hi - I have a series of 9 GAM regressions with about 5 parametric effects and three non-parametric effects in each. What is a good library or command for turning GAM outputs into publication-ready regression tables? I tried apsrtable and the mtable command in memisc but neither seemed to work with the gam output. I'd be okay with two separate tables - one for the parametric effects and

Hello. Is NN for R for Win available? It seemes that there's no NN package in contributed ones. -------------- next part -------------- An HTML attachment was scrubbed... URL: https://stat.ethz.ch/pipermail/r-help/attachments/20010310/6a58939c/attachment.html