similar to: The mysterious e1071

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

I observed the same behavior as Edward this morning with R-1.2.2 on Red Hat Linux 7.0. There appears to be a problem with the library in netpbm-9.5-5, even after hunting down the missing shhopt.h that pbmplus.h is looking for. I worked around the problem by compiling e1071 without pbm support (moved pbm.c to pbm.c.bak). Matt Matthew R. Nelson, Ph.D. Director, Information Systems Esperion

I observed the same behavior as Edward this morning with R-1.2.2 on Red Hat Linux 7.0. There appears to be a problem with the library in netpbm-9.5-5, even after hunting down the missing shhopt.h that pbmplus.h is looking for. I worked around the problem by compiling e1071 without pbm support (moved pbm.c to pbm.c.bak). Matt Matthew R. Nelson, Ph.D. Director, Information Systems Esperion

** save image Error in loadNamespace(i[[1]], c(lib.loc, .libPaths()), keep.source) : There is no package called 'class' Using 30 May 2004 developer version of R-1.9.0 trying URL `http://cran.r-project.org/src/contrib/e1071_1.4-0.tar.gz' Content type `application/x-tar' length 325388 bytes opened URL .......... .......... .......... .......... .......... ..........

Hello all, I am trying to use the "svm" method provided by e1071 (Version: 1.5-16) together with a matrix provided by the SparseM package (Version: 0.73) but it fails with this message: > model <- svm(lm, lv, scale = TRUE, type = 'C-classification', kernel = 'linear') Error in t.default(x) : argument is not a matrix although lm was created before with

This could be a bug in the e1071 svm code, but maybe not -- I guess I'll send it here anyway. It's reproducible. > x <- seq (0.1,5,by=0.05) > y <- log(x) + rnorm (x, sd=0.2) > library(e1071) > m <- svm (x,y) Process R segmentation fault at Tue Nov 20 23:34:19 2001 > version _ platform i686-pc-linux-gnu arch i686 os

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")

Hello List, I am having a great trouble using svm function in e1071 package. I have 4gb of data that i want to use to train svm. I am using Amazon cloud, my Amazon Machine Image(AMI) has 34.2 GB of memory. my R process was killed several times when i tried to use 4GB of data for svm. Now I am using a subset of that data and it is only 1.4 GB. i remove all unnecessary objects before calling

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

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,

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 all, I have a question about using categorical predictors for SVM, using "svm" from library(e1071). If I have multiple categorical predictors, should they just be included as factors? Take a simple artificial data example: x1<-rnorm(500) x2<-rnorm(500) #Categorical Predictor 1, with 5 levels x3<-as.factor(rep(c(1,2,3,4,5),c(50,150,130,70,100))) #Catgegorical Predictor

Hi everyone I am trying to do cross validation (10 fold CV) by using e1071:svm method. I know that there is an option (?cross?) for cross validation but still I wanted to make a function to Generate cross-validation indices using pls: cvsegments method. ##################################################################### Code (at the end) Is working fine but sometime caret:confusionMatrix

Hallo! I would need a code for 10-fold cross validation for the classifiers Naive Bayes and svm (e1071) package. Has there already been done something like that? I tried to do it myself by applying the tune function first: library(e1071) tune.control <- tune.control(random =F, nrepeat=1, repeat.aggregate=min.,sampling=c("cross"),sampling.aggregate=mean, cross=10, best.model=T,

Hello, I am trying to import data from Matlab.. when i looked up R documentation, it says, package "e1071" have command (read.octave) to import data from octave. but when I tried to install package by using: install.packages("e1071"); I got the following message: ( BTW, my platform is linux version 2.4.18-3 my gcc is 2.96). * Installing *source* package 'e1071' ...

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 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,

Hi everybody, I am new in e1071 and with SVMs. I am trying to understand the performance of SVMs but I face with a situation that I thought as not meaningful. I added the R code for you to see what I have done. /set.seed(1234) data <- data.frame( rbind(matrix(rnorm(1500, mean = 10, sd = 5),ncol = 10), matrix(rnorm(1500, mean = 5, sd = 5),ncol = 10))) class <- as.factor(rep(1:2,

Dear all, I executed svm calculation using e1071 library with a microarray data (http://www.iu.a.u-tokyo.ac.jp/~kadota/R/data_Singh_RMA_3274.txt). Then, I shuffled the data samples and executed svm calculation again. The results of 2 calculation were different (in SV, coefs and weights). I attached the script below. Could please tell me why this happens? If possible please tell me how to make

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