similar to: nnet formular for reproduce the expect output

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

Hola compañeros de la lista. Los molesto con la siguiente duda. En un diseño central compuesto (CCD) con dos factores (V1 y V2) y una variable de respuesta (R), utilizando valores codificados (-1.4142, -1, 0, 1, 1.4182), al aplicar la orden: rsm.segundo.orden <- rsm(R ~ Bloque + SO(V1, V2), data = DATOS.Codificados) Obtengo el siguiente modelo: R = 103.92 -2.16

I'm trying to fit a multinominal logistic model using package mlogit. I have 15 independent variables. The code looks like this: m<-mlogit(score~0|f1+f2+f3+f4+f5+f6+f7+f8+f9+f10+f11+f12+f13+f14+f15, data, reflevel="1") And it gives the following error message: Error in parse(text = x) : unexpected ')' in "score ~ 0 + alt:(f1 + f2 + f3 + f4 + f5 + f6 + f7 + f8 + f9

hi all i'm new to the area of neural networks. i've been reading some references and seem to understand some of the learning algorithms. i am very familiar with regression and would just like to see how neural nets handle this problem so i've been using the nnet package. i simply want to use a 3 layer neural net, ie 1 input, 1 hidden layer (where the hidden layer is linear, since i

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

...as promised. This describes the mdct used in my d.m.l patch. I think it is the same as the Lee fast-dct. I typed it in a kind of pseudo-TeX, 'cause the ascii art would kill me. Hope you can read TeX source; if not, ask someone who can to make a .ps/.gif/.whatever of the TeX output, and put it on a webpage or something. I'm to lazy to do it (and besides, I don't have access to TeX,

Dear all this is a question of model specification in lme which I'd for which I'd greatly appreciate some guidance. Suppose I have data in long format gene treatment rep Y 1 1 1 4.32 1 1 2 4.67 1 1 3 5.09 . . . . . . . . . . . . 1 4 1 3.67 1 4 2 4.64 1 4 3 4.87 .

Dear R users, I'd like to compute X like below. X_{i,t} = 0.1*t + 2*X_{i,t-1} + W_{i,t} where W_{i,t} are from Uniform(0,2) and X_{i,0} = 1+5*W_{i,0} Of course, I can do this with "for" statement, but I don't think it's good idea because "i" and "t" are too big. So, my question is that Is there any better idea to avoid "for" statement

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 have considered neural network to classify the health status of the cow. I found a very neatly written R codes for classification method in here<http://home.strw.leidenuniv.nl/~jarle/IAC/RRoutines/classification-example.R> . It would be very helpful if you can answer some of the questions, that I am struggling with, I have set of time series data from different animals, I use

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. The details of the model are: Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij}) y_{ij} = mu_{ij} + w_{ij} * *with* *logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b2 * x2_{ij}

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. I'm so sorry. In the last email, I forgot to say that W is also a unknown parameter in the mixed beta regression model. In any case, here I send you the correct formulation. ** Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~

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

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 would like to add .reg file to all samba clients (windows xp) in quiet mode. How can I do it ? It is possible from eg netlogon?? ---------------------------------------------------- ERROR! ... . .... bEZb??DnY prOgrAm__o nowo??iAcH w_ ?wiEcie G_iER na.jNowSzy_OdcinEk_ : co $Zykuj? tw?rcy caLL of Duty4, czym JeST The CLUB ............. poD??cz si?:

Hi all, I am trying to minimize the quardratic form w'Aw, with certain constraints. In particular, (1) A=(a_{ij}) is n by n matrix and it is symmetric positive definite, a_{ii}=1 for all i; and 0<a_{ij}<1 for i not equal j. (2) w'1=n; (3) w_{i}>=0 Analytically, for n=2, it is easy to come up with a result. For larger n, it seems difficult to obtain

I have the following time series model for prediction purposes *Loss_t = b1* Loss_(t-1) + b2*GDP_t + b3*W_(t-1)* where W_t is the usual white noise variable. So this is similar to ARMA(1,1) except that it also contains an extra predictor, GDP at time t. I have only 20 observations on each variable except GDP for which I know till 100 values. And most importantly,I have also calculated

Hello, I want use the package NUT in a script perl, i wonder how use the result of method such as BattPercent() for example. here is my code : _#!/usr/bin/perl -w_ _# server_ _use strict;_ _#use /etc/nut/dev/modules;_ _#use IO::Socket::Multicast;_ _use UPS::Nut;_ _my $message = localtime;_ _$message .=" `who` . n";_ _print "-- test : $message n";_ _my $ups = new

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

Hello, First of all I am french, so please forgive me, if there are some big language mistakes in my sentences. I think, it is the good mail address to send my question, if not please tell me and forgive me. I am working on a project, and I use the nnet library. Our customers do not want us to install R on their machine, so we just use R for making the training of our neurons network. This