similar to: SVM classification based on pairwise distance matrix

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) >

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

Good afternoon. I would like to know if there is any function in R to do LOOCV with these classifiers: 1)SVM 2)Neural Networks 3)C4.5 ( J48) 4)KNN Thanks a lot! [[alternative HTML version deleted]]

Dear list I've a generic question about how to tune an SVM I'm trying to classify with caret package some population data from a case-control study . In each column of my matrix there are the SNP genotypes , in each row there are the individuals. I correctly splitted my total dataset in training(132 individuals) and test (50 individuals) (respecting the total observed genotypic

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

Hello, I'm curious if anyone has encounted a version of this problem (and it's solution) involving finding a consistent set of scales for subsets of survey data. The goal is to obtain peoples' rankings of pairwise similarity of a large number of items, on a 1..5 scale for example, and average these across people to use as input to MDS: How similar is object A to B on a 1..5

Hi all, is it possible to create a multidemensional list? Haven't found anything in the list() help page. Thanks, Sven -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !)

Hi, There are two packages providing knn classification: class and knnTree. However, it seems both uses Eucleadian distances only. How can I uses a custom distance function with either package? Thanks, Xiao-Jun

Dear R-list! I am using the e1071 package in R to solve a binary classification problem in a dataset of round 180 predictor variables (blood metabolites) of two groups of subjects (patients and healthy controls). I am confused regarding the correct way to assess the classification accuracy of the trained svm. (A) The svm command allows to specificy via the 'cross=k' parameter to specify a

Dear all, I am having a hard time to figure out a suitable test for the match between two nominal classifications of the same set of data. I have used hierarchical clustering with multiple methods (ward, k-means,...) to classify my dat into a set number of classesa, and I would like to compare the resulting automated classification with the actual - objective benchmark one. So in principle I

hello , I am new user in R . I have datafile (class = data.frame) which has 825 columns with unique column name i want extract 200 selected column from datafile how can I do this? my datafile look like.. Mi RBN RBF nDB nX 3 2.6225979 0.53132756 -0.80599902 -1.4471864 -0.5705269 10 0.4818746 -1.72143092 -2.19579027 2.0118824 -0.5705269 12 2.8519611

Hello, How can I do a cross validation in R? Thank You!

Hi!! I am trying to figure out how to use the string kernel "stringdot" in kernlab. k <- function(x,y) { (sum(x*y) +1)*exp(-0.001*sum((x-y)^2)) } class(k) <- "kernel" data(promotergene) ## train svm using custom kernel gene.k <- ksvm(Class~.,data=promotergene,kernel=k,C=10,cross=5) # works fine in this case gene.rbf <-

Hi, I have a simple dataset with repeated measures. one factor is treatment with 3 levels (treatment1, treatment2 and control), the other factor is time (15 time points). Each treatment group has 10 subjects with each followed up at each time points, the response variable is numeric, serum protein amount. So the between subject factor is treatment, and the within subject factor is time. I ran a

R-List, I'm trying to compute pairwise distances among pairs of observations, which each pair containing data from 2 groups. There are more than 100000 unique pairs. I have programmed a distance function that has three parameters, a vector of covariates from the ith observation in Group 1, a vector of covarites from the jth observation in Group 2, and a weighting matrix. I have used

Is there a method to list the components of an object, instead of looking at the help for that method? Let me be more clear with an example data(iris) ## tune `svm' for classification with RBF-kernel (default in svm), ## using one split for training/validation set obj <- tune(svm, Species~., data = iris, ranges = list(gamma = 2^(-1:1), cost = 2^(2:4)),

Hi! I got the following runtime error when I tried to use svm method with stepclass. Error in "colnames<-"(`*tmp*`, value = c("0", "1")) : attempt to set colnames on object with less than two dimensions I repeated the same sequence of statements but this time I used the classification function used in the example, i.e., "lda" and it worked fine

Hello, I am trying to convert list of pairwise distances to a distance matrix for spatial analysis (kriging). For instance, I have something like this for each pair pf points, and I want to convert it to a matrix: point1 point2 distance 1 1 0 1 2 4 2 2 0 2 1 4 Please let me know if there is a

Hello, I have two questions concerning the RWeka package: 1.) First question: How can one perform a cross validation, -say 10fold- for a given data set and given model ? 2.) Second question What is the correct syntax for the parametrization of e.g. Kernel classifiers interface m1 <- SMO(Species ~ ., data = iris, control =

Hi I am Aadhithya I am trying to write a code to classify microarray data (AML and ALL) using SVM in R my code goes like this : library(e1071) train<-read.table("Z:/Documents/train.txt",header=T); test<-read.table("Z:/Documents/test.txt",header=T); cl <- c(c(rep("ALL",10), rep("AML",10))); model<- svm(train,cl); pred <-