similar to: Sugeestion about tuning of SVM

Dear List I'm a newbie R user. I'm utilizing the list function in order to make a var like this: clusters<-list(a=var1,b=var2....) My problem is that the total numer of variables that I need to include in my list is up to 200. I've the text string with the complete list of my variables but is too long to cut and paste in my bash shell. So is there a way too import the list from a

Dear user is the first time that I use wine. I'm trying to install with wine 1.31 a technical software for analyzing some biological data. Starting from a shell I retrieve always the same error Please could someone give to me some suggestion about what this error mean? thank you very much Guido fixme:sfc:SFC_3 0 fixme:advapi:SaferCloseLevel ((nil)) stub err:ole:create_server class

Dear list I'd like to have your opinion about my case study. I'm analizing a dataset of 9 experiments and 15 variables with the aim to highlight the variables that can majorly explain the variance between the experiments. This is an example with only 3 rows and 5 variables var1 var2 var3 var4 var5 sample5 0,067 0,005 0,008 0,100 0,005 sample6 0,069 0,001

Dear All, I am planning to run a Monte-Carlo experiment which involves to do roughly 100.000 times the following 1- Generating a sample of, say, 50.000 numbers from an ARMA or GARCH 2- Doing some regressions on the series 3- On each regression storing one special value from the results into a sequence that will be analyzed later. The experiment is calculation-intensive, and I suspect some

Dear list I wish to extract from a population genotypized for 10 SNP a subsample of the same population of size n with similar allele frequencies. Essentially i have a matrix of 200 rows (df) like this Name,Condition,rs1385699_X,rs6625163_X,rs962458_X,Rs4658627_1, sample01,Case,1,1,1,-1 sample02,Control,1,1,1,1 sample06,Control,1,-1,1,0 sample10,Case,1,1,1,0 sample11,Control,1,1,1,1

Hi all - (Re my previous question to the list, I managed to generate an ROC plot for my SVM by ranking the data using the Decision.Values property. Thanks for your responses) I have now started tuning the SVM to get optimal parameters for the RBF kernel and I ran into a problem. Whatever parameter ranges I specify, I always get the same error values for all combinations of parameters it

Hello, I am having really hard time finding a good article about simulating genotypes of cases and controls at a disease locus using R. if you guys can point me or guide me where i can find more information, it will be helpful. thanks, Claire -- View this message in context: http://www.nabble.com/genotypes-simulation-tp17065607p17065607.html Sent from the R help mailing list archive at

Dear list I'm testing a predictor and I produced nice performance plots with ROCR package utilizing the 3 standard command pred <- prediction(predictions, labels) perf <- performance(pred, measure = "tpr", x.measure = "fpr") plot(perf, col=rainbow(10)) The pred object and the perfo object are S4 with the following slots An object of class "performance"

Hi All, I was wondering if someone could help me to solve this issue with lmer. In order to understand the best mixed effects model to fit my data, I compared the following options according to the procedures specified in many papers (i.e. Baayen <http://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA

I use tune.svm to tune gamma and cost for my training dataset. I use PC, it runs very slowly. Does anyone know how to make it faster? Aimin

Hi all, I'm having this error, since I'm working with a data matrix I don't understand what's happening; I've tried several ways to solve this, even working with sparse matrix, but nothing seems to solve it, I've also tried svm (with a simple matrix 3*3 and still got the same error. > dados<-read.table("b.txt",sep="",nrows=30000) >

Hi - I am doing some monte carlo simulations comparing bayesian (using Plummer's jags) and maximum likelihood (using lmer from package lme4 by Bates et al). I would like to know if there is a way I can flag nonconvergence and exceptions. Currently the simulations just stop and the output reads things like: Error in optim(.Call("lmer_coef", x, 2, PACKAGE = "Matrix"), fn,

Hi, My question might be a little general. I have a number of values to select for the complexity parameters in some classifier, e.g. the C and gamma in SVM with RBF kernel. The selection is based on which values give the smallest cross validation error. I wonder if the randomized splitting of the available dataset into folds is done only once for all those choices for the parameter values, or

I have a data set with about 30,000 training cases and 103 variable. I've trained an SVM (using the e1071 package) for a binary classifier {0,1}. The accuracy isn't great. I used a grid search over the C and G parameters with an RBF kernel to find the best settings. I remember that for least squares, R has a nice stepwise function that will try combining subsets of variables to find

Hi, Does anyone know why the package MCRestimate is not part of the BioConductor 2.2 anymore? I am looking for package doing support vector machine analyses including (i) variable/feature selection and (ii) parameter tuning in one learning period. Many thanks, Stefan --

Hello, if I try to tune my svm with the code: Tune <- tune.svm(Data.Train, Class.Train, type="C-classification", kernel="radial", gamma = 2^(-1:1), cost = 2^(2:4)) i get a windows Messagebox with a error in the application "Rgui.exe" and the message: "Die Anweisung in 0x6c48174d verweist auf Speicher 0x00000000. Der Vorgang "read" konnte nicht auf

Hi - I am doing a monte carlo experiment that requires to do a linear regression of a matrix of vectors of dependent variables on a fixed set of covariates (one regression per vector). I am wondering if anyone has any idea of how to speed up the computations in R. The code follows: #regression function #Linear regression code qreg <- function(y,x) { X=cbind(1,x) m<-lm.fit(y=y,x=X)

Dear all, I am exploring the possibilities for automated classification of my data. I have successfully used KNN, but was thinking about looking at SVM (which I did nto use before). I have a pairwise distance matrix of training observations which are classified in set classes, and a distance matrix of new observations to the training ones. Is it possible to use distance matrices for SVM, and

Hello, Is the first time I am using SNOW package and I am trying to tune the cost parameter for a linear SVM, where the cost (variable cost1) takes 10 values between 0.5 and 30. I have a large dataset and a pc which is not very powerful, so I need to tune the parameters using both CPUs of the pc. Somehow I cannot manage to do it. It seems that both CPUs are fitting the model for the same values

Hello I am using support vector machine (from package kernlab) for a classification task (with RBF-Kernel). My data has dozens of variables and I need to identify which variables contribute most to the classification performance. What I did so far is comparing the classification performance (measured for example with the proportion of misclassified cases) of different sets of variables with