similar to: Imputation with SOM using kohonen package

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 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

Hi, I'm trying to utilize the kohonen package to build SOM's. However, trying this on my data I get the error: "Argument data should be numeric" when running the som(data.train, grid = somgrid(6, 6, "hexagonal")) function. As you see, there is a problem with the data type of data.train which is a list. When I try to convert it to "numeric" I get the error:

Hi, I have a question about using lm on matrix, have to admit it is very trivial but I just couldn't find the answer after searched the mailing list and other online tutorial. It would be great if you could help. I have a matrix "trainx" of 492(rows) by 220(columns) that is my x, and trainy is 492 by 1. Also, I have the newdata testx which is 240 (rows) by 220 (columns). Here is

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

Hi all, I would like to do cross validation in random forest using rfcv function. As the documentation for this package says: rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5, mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...) however I don't know how to build trianx and trainy for my data set, and I could not understand the way trainx is built in the package

Hi All, I am experimenting with using binary data (1, 0) in the kohonen package. I would like either 1 or 0 to be mapped in SOM, but the package gives me 1 or 0 (as expected), but something in between. I am not scaling the binary data, but still there are in-between values. Can anybody suggest a way to keep the data binary, even in the SOM plot? Thanks a lot. George

Hi all, I would like to do cross validation in random forest using rfcv function. As the documentation for this package says: rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5, mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...) however I don't know how to build trianx and trainy for my data set, and I could not understand the way trainx is built in the package

I would really like some or any advice on how I can improve (or fix??) the following analysis. I hope I have provided a completely runnable code - it doesn't produce any errors for me. The resulting plot at the end shows a pretty poor correlation (just speaking visually here) to the test set. How can I improve the performance of the mapping and prediction? Here are some of the data

Any responds?! On Wednesday, August 23, 2017 5:50 AM, Elahe chalabi via R-help <r-help at r-project.org> wrote: Hi all, I would like to do cross validation in random forest using rfcv function. As the documentation for this package says: rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5, mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...) however I

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)

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.

Is there an implementation of the SOM ('Self Organizing Map') procedure in R ? I am aware of the implementations of Sammon mapping, multidimensional scaling and, somewhat peripherally, principal curves and projection pursuit .. but not SOM per se as far as I can see. Am I missing something? --------------------- for anyone interesed in SOM I found Samuel Kaski's thesis

Hi all, I have a question about the SOM routine. You can either supply the initial representatives for the lattice yourself or else they are chosen randomly from the dataset. Is it possible to pass the random-seed as an argument somehow, when choosing the random initialisation of the lattice? As it is now, each time I run a SOM on a dataset with the same settings the resulting SOM will still

Hai, You posted the correct things here, for a quick fix i I'm buzzy with something else atm but i saw that /dev/urandom part. Add in the bind9 (named) apparmor profile # Samba DLZ /{usr/,}lib/@{multiarch}/samba/bind9/*.so rm, /{usr/,}lib/@{multiarch}/samba/gensec/*.so rm, /{usr/,}lib/@{multiarch}/samba/ldb/*.so rm, /{usr/,}lib/@{multiarch}/ldb/modules/ldb/*.so rm,

Hi, I'm trying to install bwm on my Centos box without joy so far. # yum check-update # yum install bwm Gathering header information file(s) from server(s) Server: CentOS-3 - Addons Server: CentOS-3 - Base Server: CentOS-3 - Extras Server: CentOS-3 - Updates Finding updated packages Downloading needed headers Cannot find a package matching bwm No actions to take # yum search bwm Gathering

Hi all, I used the following code and data to get auc values for two sets of predictions: library(caret) > table(predicted1, trainy) trainy hard soft 1 27 0 2 11 99 > aucRoc(roc(predicted1, trainy)) [1] 0.5 > table(predicted2, trainy) trainy hard soft 1 27 2 2 11 97 > aucRoc(roc(predicted2, trainy)) [1] 0.8451621 predicted1: 1 1 2

Hi All, I'm looking for some reference paper about SOM (self organizing map) algorithm. I tried the paper which is mentioned in the help page of function "som (package:som)": http://www.cis.hut.fi/research/papers/som_tr96.ps.Z But I can't open it for some reason. Could you please help me with it ? Thanks a lot! [[alternative HTML version deleted]]

Does anyone know of any SOM library for R? or any stand alone freeware? A search from google returns SOM_PAK 3.1 developed at Helsinki University of Technology. Is there newer version? Jun -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Sir, This query is related to randomForest regression using R. I have a dataset called qsar.arff which I use as my training set and then I run the following function - rf=randomForest(x=train,y=trainy,xtest=train,ytest=trainy,ntree=500) where train is a matrix of predictors without the column to be predicted(the target column), trainy is the target column.I feed the same data