similar to: help with tune.svm() e1071

Hi, I am trying to install the “impute” package in unix. but I get the following error message. I followed the following steps. Do you know what is causing this and how I can solve this problem? source("http://www.bioconductor.org/biocLite.R") biocLite("impute") Using R version 2.11.1, biocinstall version 2.6.10. Installing Bioconductor version 2.6 packages: [1]

Dear All , 1- I'm trying to access the values of fitted(model) after model<- tune.svm( ) but seemingly it is not poosible. How can I access to values of fitted ? However ,it is possible only after model<- svm( ) 2- How can I access to the other values such as the number of Support Vectors , gamma, cost , nu , epsilon , after model<- tune.svm( ) ? these are not possible? I

Hi, I run the following tuning function for svm. It's very strange that every time i run this function, the best.parameters give different values. [A] >svm.tune <- tune(svm, train.x, train.y, validation.x=train.x, validation.y=train.y, ranges = list(gamma = 2^(-1:2), cost = 2^(-3:2))) # where train.x and train.y are matrix

Hi, I am looking for a command in R that would force the for loop to stop after it finds what it is looking for. As an example for(i in 1:5){ for(j in 3:6){ if(i==j) # do something... break; } } And i don't want the loop to execute once i = 3 and stop. Is there a way to do this? best, salih [[alternative HTML version deleted]]

Hi All, What is the fastest way of finding if any members of vector x fall in the range of the rows of matrix y? I do not want to use two for loops as this will take forever. Any help will be appreciated, best, salih [[alternative HTML version deleted]]

Hi, Is there a quick way of finding if numbers in a vector falls in between a range defined in another matrix. As an example let x y 0 1 3 2 6 9 25 15 18 I want to check one by one whether 0,2 and 25 falls in any of the ranges in y. I am using 2 for loops but it is taking a huge amount of time. is there any other alternative way to do this? best, salih [[alternative HTML

R-help, I am trying to perform a basic anlaysis of the BreastCancer data from "mlbench" using the svm() function in "e1071". I use the following code library("e1071") library("mlbench") data(BreastCancer) BC <- subset(BreastCancer, select=-Id) pairs(BC) model <- svm(Class ~ ., data=BC, cross=10) ## plot(model, BC, ) tobj <- tune.svm(Class ~ .,

Hello all, I am using the e1071 SVM with the tune options for classification, which work pretty well, given the examples of using tune.svm function for classification. But I have not found any example to tune the SVM novelty detection (one-classification) parameters (gamma, cost, nu), for example this are some of the options I have tried with no success: obj<-tune(svm, x,y, type

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Hi, If I try to read the codes of functions in e1071 package, it gives me following error message. >library(e1071) > svm function (x, ...) UseMethod("svm") <environment: namespace:e1071> > predict.svm Error: Object "predict.svm" not found > Can someone help me on this how to read the codes of the functions in the e1071 package? Thanks. Raj

In e1071 package/svm default epsilon value is set to 0.1 and not 0.5 as documentation says. R

Hi, I ran two svm models in R e1071 package: the first without cross-validation and the second with 10-fold cross-validation. I used the following syntax: #Model 1: Without cross-validation: > svm.model <- svm(Response ~ ., data=data.df, type="C-classification", > kernel="linear", cost=1) > predict <- fitted(svm.model) > cm <- table(predict,

Dear David, Dear Friends, After any running svm I receive different results of Error estimation of 'svm' using 10-fold cross validation. What is the reason ? It is caused by the algorithm, libsvm , e1071 or something els? Which value can be optimal one ? How much run can reach to the optimality.And finally, what is difference between Error estimation of svm using 10-fold cross validation

Hi all, I've got the following error message in using e1071 svm routine... Could anybody please help me? Thank you! --------------------------------- model <- svm(y=factor(mytraindata[, 1]), x=mytraindata[, -1], probability=T) Error in if (any(co)) { : missing value where TRUE/FALSE needed In addition: Warning message: In FUN(newX[, i], ...) : NAs introduced by coercion

Hi, I have a question about the parameter C (cost) in svm function in e1071. I thought larger C is prone to overfitting than smaller C, and hence leads to more support vectors. However, using the Wisconsin breast cancer example on the link: http://planatscher.net/svmtut/svmtut.html I found that the largest cost have fewest support vectors, which is contrary to what I think. please see the scripts

Hello, I have an SVM model previously calibrated using libsvm R implementation from the e1071 package. I would like to use this SVM to predict values, from a Java program. I first tried to use jlibsvm and the "standard" java implementation of libsvm, without success. Thus, I am now considering writing data in files from my Java code, calling an R program to predict values, then gather

Full_Name: Julien Gagneur Version: 2.2.1 OS: Linux (Suse 9.3) Submission from: (NULL) (194.94.44.4) Using the SparseM library (SparseM_0.66) and the e1071 library (e1071_1.5-12) I fail using svm method with a sparse matrix. Here is a sample example. I experienced the same problem under Windows. > library(SparseM) [1] "SparseM library loaded" > library("e1071")

Hi. I'm trying to plot a pair of intertwined spirals and an svm that separates them. I'm having some trouble. Here's what I tried. > library(mlbench) > library(e1071) Loading required package: class > raw <- mlbench.spirals(200,2) > spiral <- data.frame(class=as.factor(raw$classes), x=raw$x[,1], y=raw$x[,2]) > m <- svm(class~., data=spiral) > plot(m,

svm.fit<-svm(as.factor(out) ~ ., data=all_h, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) # model fitting svm.pred<-predict(svm.fit, hh, decision.values = TRUE, probability = TRUE) # find the probability, but can not find. attr(svm.pred, "probabilities") > attr(svm.pred, "probabilities") 1 0 1 0 0 2 0

Hi, Sorry that I have many questions today. I am using svm function on about 180,000 points of training set. It takes very long time to run. However, I would like it to spit out something to make sure that the run is not dead in between. Would you please suggest anyway to do so? And is there anyway to speed up the performance of this svm function? Thank you. - adschai