similar to: SOM random seed

Hello everyone, I'm new to this list, so let me introduce myself: my name is Jonck van der Kogel and I am a graduate student at the Erasmus University of Rotterdam. I am currently working on my thesis which is on the use of artificial intelligence for large data-sets. To do an analysis of a certain data-set I want to use Kohonen's SOM algorithm. However, as I understand it, the SOM

Hi all, I was wondering whether it is possible to plot math functions, for example sin, cos or a Gaussian type function, in R, and if so, how to do it. I have been searching through the archives and the R manual but had no luck in finding any hints on how to go about this. Any help is much appreciated! Thanks, Jonck

Hi all, I'm currently working with a dataset that has quite a few missing values and after some investigation I figured that multiple imputation is probably the best solution to handle the missing data in my case. I found several references to functions in S-Plus that perform multiple imputation (NORM, CAT, MIX, PAN). Does R have corresponding functions? I searched the archives but was not

Hi all, I am running a series of experiments where after manipulating my data I run several clustering algorithms (agnes, diana and a clustering method of my own) on the data. I wanted to determine which clustering method did the best job, so therefore I had defined my own quality measure using two criteria: compactness of the data within the clusters themselves and the amount of seperation

Hi all, I have (yet another) question about a function in R. What I would like to do is test for the presence of a certain value in a vector, and have the positions that this value is at returned to me. For example, let's say I have a vector: x <- c(1,1,2,2,3,3,4,4) Now I would like a function that would return positions 3 and 4 should I test for the value "2". Or 5 and 6

Hi all, I am having a bit of trouble with the array structure of R. What I want to do is dynamically add/remove elements to an array. For example: Let's say I have created an array: > myArray <- array(c(3,8), dim=c(1,2)) > myArray [,1] [,2] [1,] 3 8 And I now want to, for example, push an element (5,6) on to this array so it will read: [,1] [,2] [1,] 3 8

I've been experimenting with the lattice graphics in chapter 3 of MASS. The code to produce figure 3.14 is: data(crabs) library(mva) lcrabs.pc <- predict(princomp(log(crabs[,4:8]))) crabs.grp <- c("B", "b", "O", "o")[rep(1:4, rep(50,4))] splom( ~lcrabs.pc[, 1:3], groups = crabs.grp, panel = panel.superpose, key = list(text =

Can anyone explain the root of my problem? When I type the following code into R, I receive 42 support vectors insted of the 21 stated in the book 'Modern Applied Statistics with S': library(MASS); library(e1071); library(class); lcrabs <- log(crabs[,4:8]); (svm(crabs$sp ~ ., data = lcrabs, cost = 100, gamma = 1)); By changing the value of gamma I can obtain only 21 support vectors,

I'm trying to learn how to create Trellis multi-panel plots, but I'm having some trouble reproducing the graphs shown in Venables & Ripley (2002) (e.g., Figs 4.14 & 4.15). Actually, everything looks fine except for the fact that I can't see any text above the individual panels. I'm using R 1.9.0 for OS-X running on Mac OSX 10.3.3, and I'm drawing the graphs into

Hi all, A short while ago I asked a question about multiple imputation and I got several helpful replies, thanks! I have untill now tried to use the packages mice and norm but both give me errors however. mice does not even run to start with and gives me the following error right away: iter imp variable 1 1 Liquidity.ratioError in chol((v + t(v))/2) : the leading minor of order 1 is not

I am trying to understand how the SOM algorithm works using library(class) SOM function. I have a 1000*10 matrix and I want to be able to summarize the different types of 10-element vectors. In my real world case it is likely that most of the 1000 values are of one kind the rest of other (this is an oversimplification). Say for example: InputA<-matrix(cos(1:10),nrow=900,ncol=10,byrow=TRUE)

Dear R users, I want to do discriminant analysis on my data. I have successfully followed the discriminant analysis in V & R on the iris data: > ir <- rbind (iris3[,,1],iris3[,,2],iris3[,,3]) > ir.species <- c(rep("s",50),rep("c",50),rep("v",50)) > a <- lda(log(ir),ir.species) > a$svd^2/sum(a$svd^2) [1] 0.996498601 0.003501399 > a.x <-

I have a data set with 10 variables, and about 8000 instances (or objects/rows/samples). In addition I have one more ('class') variable that I have about 10 instances for, but for which I wish to impute values for. I am a little confused how to go about doing this, mostly as I'm not well-versed in it. Do I train the SOM with a data object that contains just the first 10 variables

All, The kohonen predict function is returning NA for SOM predictions regardless of data used... even the package example for a SOM using wine data is returning NA's Does anyone have a working example SOM. Also, what is the purpose of trainY, what would be the dependent data for an unsupervised SOM? As may be apparent to you by my questions, I am very new to kohonen maps and am very grateful

Hi. Three years ago, I've read the question of availability of SOM library for R using Kohonen's SOM_PAK in this mailing-list. This answer was no availability. And no package dealing with SOM in CRAN. Is this situation same? Could you tell me availability this library? Best Regards.

HI, Dear R community, I am using the following codes to do the som. I tried to label the notes by the majority vote. either through mapping or prediction. I attached my output, the left one dont have any labels in the note, the right one has more than one label in each note. I need to have only one label for each note either by majority vote or prediction. Can anyone give some suggestions or

somfunc<- function (file) { aa_som<-scale(file) final.som<-som(data=aa_som, rlen=10000, grid=somgrid(5,4, "hexagonal")) pdf(file="/home/cdu/changbin/file.pdf") #output graphic file. plot(final.som, main="Unsupervised SOM") dev.off() } I have many different files, if I want output pdf file with the same name as for each dataset I feed to the function

Hi All, I am still learning R, but making, IMO, great strides. I learned about Kohonen/Self-Organizing Maps in class and I would like to try to replicate some of the things we have seen in class. Below is my code. I am trying to create a u-matrix. In the documentation on page 9 it appears the type of plot, dist.neighbours should do the trick, however, I am getting an error: (Error in

Hello R users, I'm using SOM() to cluster a gene expression data set the syntax i used was dataGrid <- c(somgrid(xdim = 3, ydim = 3, topo = c("rectangular","hexagonal"))) dataClusters <- SOM(dataMatrix, grid = dataGrid) plot(dataClusters) it seems that this works just fine but the thing i can't figure out is how to determine where each data point has been

Dear list, I read the SOM package manual but I don't understand how to perform (for example) 1) the SOM analysis on Iris data 2) with a visualization similar to that of figure 7 in http://www.cis.hut.fi/projects/somtoolbox/package/papers/techrep.pdf Any suggestion? Thanks in advance, Gianandrea -- View this message in context: