similar to: Why cant my S4 class have a slot named `C`?

Hi Sadaf, Out of curiosity, what sorts of things have you tried to fix this? For example, after playing around with this a bit, if I remove your "eps" parameter from your `ranges` list, it works. Perhaps you should try tweaking the values you pick for your parameters. You don't even have to put it in the `tune` function to get an idea of the ranges you should use: R>

Hi I am doing this sort of thing: POLY: > > obj = best.tune(svm, similarity ~., data = training, kernel = "polynomial") > summary(obj) Call: best.tune(svm, similarity ~ ., data = training, kernel = "polynomial") Parameters: SVM-Type: eps-regression SVM-Kernel: polynomial cost: 1 degree: 3 gamma: 0.04545455 coef.0: 0

Hi Abbas, Before I try to give you answers, I just want to mention that you should send R related reqests to the R-help list, and not me personally because (i) there's a greater likelihood that it will get answered in a timely manner, and (ii) people who might have a similar problem down the road might benefit from any answer via searching the list archives ... anyway: On Sep 5, 2009, at

There are too many arguments in the signature of the my_inla generic. The signature defaults to every argument in the formals, which are probably already over-specified. Typically, one defines a generic with the formals (x, ...), or perhaps in this case (x, model, ...), but more on that below Unrelated to this issue, the my_inla generic is defined incorrectly. It should call

Hi All, I'm attempting to use eps-regression or nu-regression SVM to compute probabilities but the predict function applied to an svm model object returns values outside [0, 1]: Variable Data looks like: Present X02 X03 X05 X06 X07 X13 X14 X15 X18 1 0 1634 48 2245.469 -1122.0750 3367.544 11105.013 2017.306 40 23227 2 0 1402 40 2611.519 -811.2500 3422.769 10499.425 1800.475 40 13822 3 0 1379

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, I am trying to use svm for regression data. this is how my data looks like: >dataTrain x y z 1 4 6 2 5 4 3 7 5 >classTrain a 2 3 4 >dataTest x y z 1 7 2 2 8 3 >classTest a 3 4 5 building the model model<-svm(dataTrain,classTrain,type="nu-regression") pred <- predict(model, dataTest) > pred 1 2 3.008842 3.120078 I

Hi I am trying to fit an svm to predict speech recognition errors. I am using best.svm like this: svm.model = best.svm(data[1:3000,1:23],data[1:3000,24],tunecontrol = tune.control()) I got this: > print(svm.model) Call: best.svm(x = data[1:3000, 1:23], tunecontrol = tune.control(), data[1:3000, 24]) Parameters: SVM-Type: eps-regression SVM-Kernel: radial cost: 1

Hi, I was wondering if there was a way to store the results of a computationally expensive "getter" call on an S4 object, so that it is only calculated once for each object. Trivial example: let's say I want to cache the "expensive" area calculation of a square object. setClass("Square", representation( length='numeric',

Dear all, I did some more work in this. - put all the metadata in a dedicated subclass (see metadataclass branch in github repository). This doesn't solve the problem. - test the code under R 3.1.2. This gives the same problem. So this is NOT a bug introduced by R 3.2.0 :-) - Profiling the code. Below the summary (code is on GitHub). It seems like most of the time is spent by paste(). Note

Dear all, I'm trying to create a virtual S4 class with some subclasses. I noticed that adding slots to this class increases the memory use and slows the functions down. Note that I'm adding very small slots (integer or character both of length 1). I've made a reproducible example at https://github.com/ThierryO/testvirtualclass. The R CMD check --as-cran fails on the tests. Some of

I got the data working, but now I got another problem with KSVM: line search fails -2.793708 -0.5831701 1.870406e-05 -5.728611e-06 -5.059796e-08 -3.761822e-08 -7.308871e-13Error in prob.model(object)[[p]]$A : $ operator is invalid for atomic vectors On Tue, Jul 7, 2009 at 6:45 PM, Steve Lianoglou<mailinglist.honeypot at gmail.com> wrote: > Hi, > > On Jul 7, 2009, at 6:44 PM,

Hello users! I'm calculating a simple model using svm(...) from the e1071 package. So far so good, with a linear kernel I'm getting 5 SVs. When plotting the result I see very well separated data clouds, but the underlying color is constantly pink, so as far as I understand no class separation is shown... I would be happy if anyone could explain me this behaviour because I think I have a

Dear list. I using the SVM-methods from the e1071, but I can't get the probabilities when predicting. Code: x <- matrix(rbinom(100, 10, 0.3), ncol=2) y <- apply(x, 1, sum) fit <- svm(y ~ x, method = "C-classification", kernel = "radial", probability = TRUE) predict(fit, x, probability=TRUE) Here predict doesn't containing any probabilities (not as attributes

In R 1.9.0 running under Solaris 2.9 on a SunBlade 100, with "Sun WorkShop 6 update 2 C++ 5.3 2001/05/15" as the C++ compiler, I just did > install.packages("e1071") The output includes these lines, which I have wrapped to fit nicely in mail: ** libs cc -I/users/local/lib/R/include -I/usr/local/include -KPIC -xlibmil \ -dalign -xO4 -c cmeans.c -o cmeans.o cc

Hi R users, I'm trying to run a SVM - regression using e1071 package but the function svm() all the time apply a classification method rather than a regression. svm.m1 <- svm(st ~ ., data = train, cost = 1000, gamma = 1e-03) Parameters: SVM-Type: C-classification SVM-Kernel: radial cost: 1000 gamma: 0.001 Number of Support Vectors: 209

Dear R-community, I was using SVM regression (svm {e1071}) for predictions of single soil properties of a huge data set (3000 samples). There are for the eps-regression using the radial basis kernel three optimization parameters needed. To make things easier (using only two optimization parameters and not loosing performance) I wanted to use LS SVM regression (lssvm{kernlab}). But it

I'm trying to run an epsilon regression model, and am comparing the results between e1071 and kernlab. I believe that I'm calling the ksvm and svm functions the same way but I'm getting different results: library(e1071); library(kernlab) ksvm(x=1:100, y=(1:100)/5, type="eps-svr", kpar=list(sigma=1)) svm(x=1:100, y=(1:100)/5, type="eps-regression", gamma=0.5) I

All, I am considering creating an S4 class whose slots (2) are both S4 classes. ?Since an S4 slot can be an S3 class I figure this can be done. ?However, the correct syntax of which I am unsure. ?Reviewing the docs I have come to the following conclusion: SetClass('myfoo', ? ? ? ? ? ? ? ? ? slots = (foo1, foo2)) Without a type I believe each slot is .Data. ?A get method on the above

If do: > library("e1071") > example(svm) I get: svm> data(iris) svm> attach(iris) svm> ## classification mode svm> # default with factor response: svm> model <- svm(Species ~ ., data = iris) svm> # alternatively the traditional interface: svm> x <- subset(iris, select = -Species) svm> y <- Species svm> model <- svm(x, y) svm>