similar to: neural network not using all observations

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

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

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

Dear all, Objective: I am trying to learn about neural networks. I want to see if i can train an artificial neural network model to discriminate between spam and nonspam emails. Problem: I created my own model (example 1 below) and got an error of about 7.7%. I created the same model using the Rattle package (example 2 below, based on rattles log script) and got a much better error of about

Dear all This is a follow-up to an earlier posting today regarding a regular expression question. In the meantime, this is the best approximation I could come up with and should give you a better idea what I am talking about. a<-c("<w AT0>a <w NN1>blockage <w CJC>and <w DT0>that<c PUN>.", "<w AT0>a <w NN1>blockage <w CJC>and

Hello All, Please consider the following: TotEmp<-c(19,6,1,1,8,44,2,33,48,1) ClusterType<-c("AGF","CNS","OSV","RTL","RTL","TRN","REL","ACC_CLUST","RTL","WHL") Taz<-c(0,0,0,100,100,100,101,101,102,103)

So im here now b/c im incredibly frustrated. Please consider the following: #Try 1 Data_<-list() Sn<-1:12 for(sn in Sn){ for(i in 1:10){ Data.X <- rnorm(100,0,10) Data_[[paste(sn,i,sep="-")]]<-Data.X } } ##Try 2 Data_<-list() Sn<-1:12 for(sn in Sn){ for(i in 1:10){ Data.X <- rnorm(100,0,10) Data_[[sn]][[i]]<-Data.X } } In Try 1 i am able to append

Hello all, I am trying to use the tapply function to sum some values and change the column names of the resulting vector. I input Emp Et 1 10565 ACC 2 7515 ADM 3 625 AGF 4 6243 CNS 5 12721 EDU 6 3924 FIN 7 18140 HLH 8 3686 INF 9 15841 MFG 10 243 MIN 11 1864 MNG 12 4664 OSV 13 5496 PRF 14 4988 PUB 15 2166 REC 16 2153 REL 17 16082 RTL 18 3582 TRN 19 757 UTL 20

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

Dear all I am trying to compare the performances of several methods using the AUC0.1 and not the whole AUC. (meaning I wanted to compare to AUC's whose x axis only goes to 0.1 not 1) I came to know about the Mcneil Hanley test from Bernardo Rangel Tura and I referred to the original paper for the calculation of "r" which is an argument of the function cROC. I can only find the

I'm creating my own package for personal and I'm having trouble getting it to a point where R (v 2.3.1) will recognise it. I've followed two different tutorials for how to create the package structure and the DESCRIPTION file ( http://web.maths.unsw.edu.au/~wand/webcpdg/rpack.html , http://www.maths.bris.ac.uk/~maman/computerstuff/Rhelp/Rpackages.html#Lin-Lin ). I'm still getting

Hi all, I took a little bit of stock in light of preparing the re-launch of the Virtualization SIG as per the board decision recently and noticed that there are quite a few old and out-of-date wiki pages lying around In particular the following pages seem very old (Iast edited in 2007) * http://wiki.centos.org/SpecialInterestGroup/Virtualization *

Dear all, I try to compare the performances of several parameters to diagnose lameness in dogs. I have several ROC curves from the same dataset. I plotted the ROC curves and calculated AUC with the ROCR package. I would like to compare the AUC. I used the following program I found on R-help archives : From: Bernardo Rangel Tura Date: Thu 16 Dec 2004 - 07:30:37 EST

I might be missing something but I thought that AUC was a measure for comparing ROC curves, so there is nothing else needed to "compare" them. The larger AUC is the higher correlation of 2 variables compared. No other measures or calculations are needed. Jarek Tuszynski -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On

Please help. I found the code in the archive. The author of this script says: "The first function (seROC) calculate the standard error of ROC curve, the second function (cROC) compare ROC curves." Can some one explain to me what are the na, nn and r parameters which are used as the input to the following two functions? Thanks much in advance. > From: Bernardo Rangel Tura >

Hi, I have a question on read.table. I have a dataset with 273,000 lines and 195 columns. I used the read.table to load the data into R: trn<-read.table('train1.dat', header=F, sep='|', na.strings='.') I found it takes forever. then I run 1/10 of the data (test) using read.table again. And this time it finished quickly. So, there might be something wrong in my data

Hi, this is a repost of a previous question - since then I have tried rebuilding my Xen0 kernel numerous times and trying different configurations, but I haven''t yet ever got a single DomU to boot so I really don''t even know what is supposed to happen, let alone how to start debugging it... Can someone please speculate on how to debug this further? It''s a gentoo

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

Hi Guys/Girls, I have recently been trying to implement samba into our environment here as a replacement for ftp clients. What I am finding is that windows explorer (NT W/S 4.0) will not list all the files in the directory or in fact all the sub directories either. It is quite unusual, one of the shares we have contains two directories sun and suntst. They are basically mirrors of each other (

Hello! I have a function: zywnoscCalosc<- function( jedzenie, n1, n2, n3, n4, d1, d2, d3, d4 ) { ndf <- data.frame(nn1=n1,nn2=n2,nn3=n3,nn4=n4) ddf <- data.frame(dd1=d1,dd2=d2,dd3=d3,dd4=d4) for (i in 1:length(n1)){ wekt_n = ndf[i,] wekt_n_ok = wekt_n[!is.na(wekt_n)] dl_n = length(wekt_n_ok) wynik = (1*wekt_n_ok)/(1*dl_n) } } and I get an error like this: Error in 1 * wekt_n_ok :