similar to: handling constant factors in prediction using svm

Hello All, I have a series of steps that needs to be run many times. Hence I put them all into a function. There is no problem in function creation, but when I call the function, the steps are not getting executed or only the first step gets executed. What possibly could be the reason? Sample Function and the result: fun <- function () { # Package load into R; a <-

Hi, I am new to R and here is what I am doing in it now. I am using machine learning technique (svm) to do predictive modeling. The data that I am using is one that is bound to grow perpetually. what I want to know is, say, I fed in a data set with 5000 data points to svm initially. The algorithm derives a certain intelligence (i.e.,output) based on these 5000 data points. I have an additional

Hello, I am trying to use SVM from e1071 package for doing binary classification and I am having problems in prediction using SVM. I ran a SVM for X~as.factor(X1)+as.factor(X2)+as.factor(X3),data=data1,cross=10. str(data1) gives me data.frame': 5040 obs. of 5 variables: $ X4: int 1 2 3 4 5 6 7 8 9 10 ... $ X : int 0 1 0 1 0 0 1 1 1 1 ... $ X2: int 1 8 15 18 1 14 10 9 8 8 ... $ X3

Hi, I am trying to access folders on a windows system on a linux system using the command, smbmount '//a.b.c.d/CCViews/abcd/abcd_Linux_dev' '/root/pqrs/LinuxDev' -o username=abcd/<domain>,uid=abcd,gid=abcd This prompts for a password and I give the correct domain password here. It gives me the error: 21896: tree connect failed: ERRDOS - ERRnosuchshare (You specified an

dear sir/madam, while i am trying to convert the data into timeseries using xts command.i am getting this error. please help me to resolve this issue xts(mydata$MCP, as.Date(rdate, format='%d-%m-%Y') + xts(mydata$MCP, as.Date(rdate, format='%d-%m-%Y') Error: unexpected symbol in: "xts(mydata$MCP, as.Date(rdate, format='%d-%m-%Y') xts"

Hi, I have been trying to make a surface plot using R - I came across functions like plot.surface, surface but I could not use these functions as they seem to be in FOO package which I cannot load. I also want to make a plane that fits the surface plot. Can anyone help? divya [[alternative HTML version deleted]]

Hi, I am getting the follwoing error while installing gsubfn_0.6-4.tar.gz library for R. R version is 2.15.1 and i am installing on Redhat linux version 2.6.18-238.9.1.el5 (mockbuild at x86-002.build.bos.redhat.com<mailto:mockbuild at x86-002.build.bos.redhat.com>) (gcc version 4.1.2 20080704 (Red Hat 4.1.2-50)) * installing to library ?/home/mapred/installables/R/library? * installing

Hi, I am struggling with using R CMD BATCH command. Kindly suggest solution to the following problem. I have a function named CinC with accept two input parameters. This can be shown as: CinC <- function(start, end) where start and end both are character strings. Please suggest me how can I run this function using command R CMD BATCH. Currently I am trying to do like this -> R CMD

Hi I am working on R 2.5.0 on window. I am trying to provide a Hyper-link to the user as a result, I have tried using gtkLinkButton to exercise the facility, however, i am not able to perform the required task, i.e. when I clicked on the LinkButton actually nothing happened. I have gone through the documentation for the same and found that GtkLinkButtonUriFunc is a function which is require to

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

Dear R-Users! I am using the svm function (e1071 package) to classify two groups using a set of 180 indicator variables. Now I am confused about the cross-validation procedure. (A) On one hand I use the setting cross=10 in the svm function to run 10 cross-validation iterations and to get an estimate of the svm's performance in prediction. (B) On the other hand most tutorials I found

Hi All, I am using ksvm method in kernlab R package for support vector machines. I learned the multiclass one-against-one svm from training data and using it to classify new datapoints. But I want to update/finetune the 'svm weights' based on some criteria and use the updated svm weights in the predict method framework. I don't know if its possible or not, how do classify new

Hi All, 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 `contrasts<-`(`*tmp*`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels The training file is a

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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Dear community, I'm performing svm-regression with svm at library e1071. As I wrote in another post: "svm e1071 call - different results", I get different results if I use the svm.default rather than the svm.formula, being better the ones at svm.formula I've debugged both options. While debugging the svm.formula, I've seen that when I reach the call: ret <-

Hello, I'm using the e1071 library for SVM functions. I can quickly train an SVM with: svm(formula = label ~ ., data = testdata) That works well. I want to tune the parameters, so I tried: tune.svm(label ~ ., data=testdata[1:2000, ], gamma=10^(-6:3), cost=10^(1:2)) THIS FAILS WITH AN ERROR: 'names' attribute [199] must be the same length as the vector [184] I don't

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Hello! We are new in using R. We use the SVM module from the library ''e1071'' for training. Problem formulation: a classification has been performed using SVM module (linear kernel). Later, a new data set (test set) comparable to the training data shall be scaled in the same way as the training set (using the same scaling parameter set, but without using the SVM again

I dont know how to tell to the function 'svm' in this two cases: tune.svm or tune (svm...) the type (I want regression, but by default it works with classification) and the specify kind of kernel (by default it work with radial)... thank you so much!!! -- View this message in context: http://r.789695.n4.nabble.com/How-to-use-tune-svm-or-tune-svm-for-regression-tp4643331.html Sent