similar to: Multiple regressions

Hi Could you please explain the line that I got from the documentation of R? does it mean that there is a difference between using and not using the formula interface with SVM ?: If the predictor variables include factors, the formula interface must be used to get a correct model matrix. Cheers, Amy _________________________________________________________________ View photos of

Hi, I understand from help pages that in order to use a data set with svm, I have to divide it into two files: one for the dataset without the class label and the other file contains the class label as the following code:- library(e1071) x<- read.delim("mydataset_except-class-label.txt") y<- read.delim("mydataset_class-labell.txt") model <- svm(x, y, cross=5)

Dear all, after having calibrated a svm model through the svm() command of the e1071 package, is there a way to i) represent the modeled relationships between the y and X variables (response variable vs. predictors)? ii) rank the influence of the predictors used in the model? Right now I am more interested in regression models, but I guess this would be useful for classification too. Thank

Hi everybody, I am using the contour function to draw isobares. And i would like to to bold contours for an interval of 500m and weak contour for an interval of 100m. Can someone help me with this? Thanks a lot Karine _________________________________________________________________ [[elided Hotmail spam]] [[alternative HTML version deleted]]

Hi, The answers to my previous question about nominal variables has lead me to a more important question. What is the "best practice" way to feed nominal variable to an SVM. For example: color = ("red, "blue", "green") I could translate that into an index so I wind up with color= (1,2,3) But my concern is that the SVM will now think that the values are

Dear R users, I'm running a SVR in package e1071 but I did not able to calculate the parameters w and b of the regression. I don't know how to do that and if it is possible to do it with this package. Someone have some idea. Any help would be much appreciated. Marlene [[alternative HTML version deleted]]

hi, could you please tell me what kind of cross validation that SVM of e1071 uses? Cheers, Amy _________________________________________________________________ View photos of singles in your area! Looking for a hot date? [[alternative HTML version deleted]]

Hi useR's, I am resending this request since I got no response for my last post and I am new to the list so pardon me if I am violating the protocol. I am trying to use the "Kernlab" package for training and prediction using SVM's. I am getting the following error when I am trying to use the predict function: > predictSvm = predict(modelforSVM, testSeq); Error in

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

Hi, Could you please tell me whether there is a difference in the result of svm when using formula instead of class label? Cheers, Amy _________________________________________________________________ Browse profiles for FREE! Meet local singles online. [[alternative HTML version deleted]]

Hi, I am studying using SVM functions of e1071 package to do prediction, and I found during the training data are "factor" type, then svm.predict() can predict data directly by categories; but if response variables are "numerical", the predicted value from svm will be continuous quantitative numbers, then how can I connect these quantitative numbers to categories? (for

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Let's say I had a matrix like this: library(Ryacas) x<-Sym("x") m<-matrix(c(cos (x), sin(x), -sin(x), cos(x)), ncol=2) How can I use R to obtain the eigenvalues and eigenvectors? Thanks, John [[alternative HTML version deleted]]

Hi, I am trying to use tune.svm in e1071 package. the command i use is tobj <- tune.svm(labels, data= data, cost = 10^(1:2)) Should the last column of the 'data' contain the labels as well? I want to use the linear kernel. But it gives me the error "Error in model.frame.default(formula, data) : 'data' must be a data.frame, not a matrix or an array" Do you know why

Hi, I am new to R and I want to know how to use C code which contains two functions one called inside another.I know that how to use C code in R if it has only one function but dont know how to do it in above case. I want to use the same in R .My C code is as follows. //#include <R.h> void sayHello(); void g(); void sayhello() { Rprintf("Hello world %d\n",global); } void

Hello, I have 12 sample each sample has got 1000 observation, i.e I have a matrix X with 1000 rows and 12 columns! m <- svm(t(X)) p <- predict (m) Can anyone tell me how to use svmtrain() in R! Many Yhanks, Samuel [[alternative HTML version deleted]]

Hi everyone, Can someone please help me in these questions?: 1)if I use crossvalidation with svm, do I have to use this equation to calculate RMSE?: mymodel <- svm(myformula,data=mydata,cross=10) sqrt(mean(mymodel$MSE)) But if I don’t use crossvalidation, I have to use the following to calculate RMSE: mymodel <- svm(myformula,data=mydata) mytest

Dear R users, I am trying to apply SVM regression for a set of microarray data. I am using the function svm() under the package {e1071}. Can anyone tell me what the *residuals *value represents? I have some observed values *y_obs* for the parameter that I want to estimate and I would expect that *svm$residuals = y_obs - svm$fitted. *However, this does not happen...Does anyone have any idea on

Hello, I hope you can help me! I`m trying to tune svm parameters: cost and gamma for a landsat image classification, but I get an error and I can't understand it. I write this: > tune(svm, Class~., data = mdt01bis, ranges = list(gamma = 2^(-15:3), cost > = 2^(-5:15))) and R gives: Error en predict.svm(model, if (!is.null(validation.x)) validation.x else if (useFormula)

Hi I'm trying to predict using a model I fitted with SVM. I constructed the model (called Svm) using a training set, and now I want to use a test set (called BankTest) for prediction. The response variable is in the first column of BankTest. > SvmPred = predict(Svm, BankTest[,-1], probability=TRUE) > SvmPredRes = table(Pred = SvmPred, True = BankTest[,1]) I get this error: Error in