similar to: using neural network in R (nnet)

Hi All! This time I'm using Nut 2.2.0, Debian etch and kernel v. 2.6.23.9. My hardware config is quite simple. There are 2 PCs connected to my PowerMust 600VA. One is mensioned above the other is a Windows PC using Winnut 2.0.0b. The Linux PC is connected to the UPS via a serial cable and acts as the server. The problem is that both upsmon and winnut disconnect regularly but in random

can u explain me, how it works your code??? please. i´m also doing a simple perceptron for homework on R and i dont know where to start. [[alternative HTML version deleted]]

Hello there, I'm back again with another question about the neural network package. I'm having trouble getting the network to run for the maximum number of iterations. It always stops early, usually after 100 iterations claiming to have converged at an answer. Now, for my purposes I want it to run for the entire number of epochs, and I'm been looking at modifying the abstol

I am new to R and use nnetTs - calls. If a time series of let's say 80000 Datapoints and did call nnetTs I want make a new net for the old ponts plus the next 1000 points (81000 datapoints total) what would again cost much calculation time. So I want to pre-init the new net with the former wonnen net to reduce the necessary iteration numbers. Is thee a possibility to do that and how? i.e.:

HI, Dear R community, I am using the nnet to fit a neural network model to do classification on binary target variable (0, 1). I am using the following codes: nnet.fit<-nnet(as.factor(out) ~ ., data=train, size=5, rang=0.3, decay=5e-4, maxit=500) I want to know what is the activation function for the original inputs, is it sigmoid activation function? and what is the output activation

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]]

Greetings R Help Group, How does one effect a multiphasic logistic growth model with 4 phases (e.g. Koops 1986; Weigel, Craig, Bidwell and Bates 1992; Grossman and Koops 2003) with R. Before writing to the group, the R help archives were searched, the web was searched with Google, Venables and Ripley 2002 was consulted, Pinheiro and Bates 2000 was consulted, Bates and Watts 2007 was bought and

Do you send information about lactation curve analyse with no linear mixed model, with fixed effects (herd, year season, parity) and random effects (cow)?. Than you very much Mario Fernando [[alternative HTML version deleted]]

Hello, I am trying to fit a nonlinear model of the form of: A*x^b*exp(-c*x) This represents a lactation curve. I have a bunch of cows, so I want COW to be a random effect. I have been trying the following code with very littel success: > fm1 <- nlme(yield ~ A*(DIM^B)*(exp(-C*DIM)), + data = group, + fixed = A + B + C ~ 1, + start = c(A = 20, B =

Hi, I need help writing a function I capture seal pups mutliple times during the lactation season in order to monitor their growth rate. When I release them, the recovery (mother-pup) time is not the same for all individuals. I want to know if individuals that recover their mother the fastest are the ones with the highest growth rates. So, I noted at every release if the pup

>>>>> Gustavo Fernandez Bayon <gbayon at gmail.com> >>>>> on Thu, 24 Aug 2017 16:42:36 +0200 writes: > Hello, > While doing some enrichment tests using chisq.test() with simulated > p-values, I noticed some strange behaviour. The computed p-value was > extremely small, so I decided to dig a little deeper and debug >

> On 25 Aug 2017, at 11:23 , Jari Oksanen <jari.oksanen at oulu.fi> wrote: > > It is not about "really arge total number of observations", but: > > set.seed(4711);tabs <- r2dtable(1e6, c(2, 2), c(2, 2)); A11 <- vapply(tabs, function(x) x[1, 1], numeric(1));table(A11) > > A11 > 0 1 2 > 166483 666853 166664 > > There are

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

It is not about "really arge total number of observations", but: set.seed(4711);tabs <- r2dtable(1e6, c(2, 2), c(2, 2)); A11 <- vapply(tabs, function(x) x[1, 1], numeric(1));table(A11) A11 0 1 2 166483 666853 166664 There are three possible matrices, and these come out in proportions 1:4:1, the one with all cells filled with ones being most common. Cheers, Jari

Hi STATISTICA has a function called "Two-way joining" (see http://www.statsoft.com/TEXTBOOK/stcluan.html#twotwo) and the reference material states that this is based on the method as published by Hartigan (found this paper: http://www.jstor.org/pss/2284710 through wikipedia). What is the relationship (if any) between the "heatmap" function in R and this technique? Is there an

Hi all, I'm a PhD student at sydney uni and am trying to run a non linear mixed model program to obtain estimates of parameters describing dairy cow lactation curves. At present, I have been able to get the data to converge using the S plus (S plus 2000) nlme function. However, when I put the same data into R (R 1.9.0), add in the nlme package and run the code, it does not converge by the

Hi all, Is there any code to run fixed effects Tobit models in the style of Honore (1992) in R? (The original Honore article is here: http://www.jstor.org/sici?sici=0012-9682%28199205%2960%3A3%3C533%3ATLALSE%3E2.0.CO%3B2-2) Cheers David [[alternative HTML version deleted]]

I'm not sure if this is the right mailing list. My apologies if I'm sending the question to the wrong mailing list. (The r-packages list moderator suggested using the R-help list.) My question is about the "chickwts" data in the datasets package. According to help(chickwts), the source of the data is ?Anonymous (1948) Biometrika, 35, 214. But this reference seems irrelevant.

Greetings, I'm working on some analyses where I need to calculate wilcox tests for paired samples. In my current literature search I've found a few papers on sample size determination for the wilcox test notably: Sample Size Determination for Some Common Nonparametric Tests Gottfried E. Noether Journal of the American Statistical Association

Dear all, I would like to run a simple regression model y~x1+x2+x3+... The problem is that I have a lot of independent variables (xi) -- around one hundred -- and that some of them are categorical with a lot of categories (like, for example, ZIP code). One straightforward way would be to (a) transform all categorical variables into 1/0 dummies and (b) enter all the variables into an lm model.