similar to: Cannot scale data

Hello everybody! I have a question. I am working with R and I am loading a .txt from my computer. This file contains a set of features from patients with numerical values and the score, that determines the gravity of the disease( from 0 to 3). Due to I want to obtain a classification of my patients using SVM,neural networks or C.4.5, I think I need to "convert" the numeric class to

Hello! I have a question. I am working with createFolds: folds<- trainControl(method='cv', index=createFolds(data$Score,list = TRUE)) I need to iterate over folds to extract the indexes from each fold. For example, if I do folds$index$Fold01, it contains: 5 11 17 29 44 50 52 64 65 I need to iterate over each $Fold_i to extract the indexes, but I can't do it because I

I have a question about data mining. I have a dataset of 70 instances with 14 features that belong to 4 classes. As the number of each class is not enough to obtain a good accuracy using some classifiers( svm, rna, knn) I need to "oversampling" the number of instances of each class. I have heard that there is a method to do this. It consists in generating these new instances as follows:

Good morning all. I am doing bagging with package caret. I need bagging for a classification problem. I am working with " bag". bag(x, y, B = 10, vars = NULL, bagControl = bagControl(), ...) bagControl(fit = NULL, predict = NULL, aggregate = NULL, downSample = FALSE) My fit function is: svmFit <- function(x, y, ...) { library(e1071)

Hello! Does anyone know how to apply bagging for SVM? ( for example) I am using adabag package to execute bagging but this method, "bagging", works with classification trees. I would like to apply my bagging to other classifiers as SVM,RNA or KNN. Has anyone do it? Thanks!! [[alternative HTML version deleted]]

dear all, I am a student in cs college. I would like to know how to plot infinte number of genes after using the svm. the data set i have consists of x which is a matrix of 39 cancer patients [rows] and 2000 gene names [colmns]. each cell is the value of the gene for a particular patient. there are two types of cancer people representedas factor y. here is the code: library(e1071) #load

Good afternoon. I would like to know if there is any function in R to do LOOCV with these classifiers: 1)SVM 2)Neural Networks 3)C4.5 ( J48) 4)KNN Thanks a lot! [[alternative HTML version deleted]]

Hello guys! I am working with some classifiers ( SVM,C4.5,RNA,etc) using 10-C.V. Once I have the model of each one, I make the validation of these models in one dataset. Then,with my model and the dataset, I extract a confusion matrix to know the capacity of prediction from the model. And finally, I extract the accuracy of this prediction based on the diagonal from the confusion matrix. The

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

Dear Community! I'm using the svm method of package e1071 for classifying my data. This really works fine, but however I have to work after creating the support vectors and the parameters with unscaled data. So the problem is when I try to train the classifier with the option "scale=F" the result is quite poor, so training with scaled data is essential. The rescaling of the support

I run the following code, all other is ok, but plot(m.svm,p5.new,As~Cur) is not ok Anyone know why? install.packages("e1071") library(e1071) library(MASS) p5 <- read.csv("http://www.public.iastate.edu/~aiminy/data/p_5_2.csv") p5.new<-subset(p5,select=-Ms) p5.new$Y<-factor(p5.new$Y) levels(p5.new$Y) <- list(Out=c(1), In=c(0)) attach(p5.new)

I run the following code, all other is ok, but plot(m.svm,p5.new,As~Cur) is not ok Anyone know why? install.packages("e1071") library(e1071) library(MASS) p5 <- read.csv("http://www.public.iastate.edu/~aiminy/data/p_5_2.csv") p5.new<-subset(p5,select=-Ms) p5.new$Y<-factor(p5.new$Y) levels(p5.new$Y) <- list(Out=c(1), In=c(0)) attach(p5.new)

Hi-- While I agree that we cannot agree on the ideal algorithms, we should be taking practical steps to implement microarrays in the clinic. I think we can all agree that our algorithms have some degree of efficacy over and above conventional diagnostic techniques. If patients are dying from lack of diagnostic accuracy, I think we have to work hard to use this technology to help them, if we

Hi, More questions in my ongoing quest to convert from RapidMiner to R. One thing has become VERY CLEAR: None of the issues I'm asking about here are addressed in RapidMiner. How it handles misisng values, scaling, etc. is hidden within the "black box". Using R is forcing me to take a much deeper look at my data and how my experiments are constructed. (That's a very

The tarballs R-devel_2005-02-16.tar.gz, R-devel_2005-02-15.tar.gz and R-devel_2005-02-14.tar.gz all had the property that when I run ./configure on them I get the following error message: config.status: error: cannot find input file: po/Makefile.in.in which terminates the configure script. After R-devel_2005-02-15.tar.gz was posted but before R-devel_2005-02-16.tar.gz was posted, Jeff Gentry

Hello, I have a very unusual situation with an SVM and wanted to get the group's opinion. We developed an experiment where we train the SVM with one set of data (train data) and then test with a completely independent set of data (test data). The results were VERY good. I found and error in how we generate one of or training variables. We discovered that it was indirectly influenced

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

Hi, If I do a standard svm regression with e1071 x <- seq(0.1, 5, by = 0.05) y <- log(x) + rnorm(x, sd = 0.2) m <- svm(x, y) we can do predict(m,x) to get the fitted values. But what if I wan tho get them by hand? Seem to me like it should be w = t(m$coefs)%*%m$SV x.scaled = scale(x, m$x.scale[[1]], m$x.scale[[2]]) t(w %*% t(as.matrix(x.scaled))) - m$rho but this is wrong If i

Dear All, I've a question about scaling the input variables for an analysis with svm (package e1071). Most of my variables are factors with 4 to 6 levels but there are also some numeric variables. I'm not familiar with the math behind svms, so my assumtions maybe completely wrong ... or obvious. Will the svm automatically expand the factors into a binary matrix? If I add numeric

Hello all. I am using method "bag()" to develop a bagging with my dataset. When I do bag( dataP, dataP$Score, B=10) I obtain this error: error en bag.default(Score ~ ., data = dataP, B = 10) : entrada en evaluacion: recursivo por defecto o problemas anteriores? ( in english, evaluating input: default recursive or previous problems?) Any idea? Thanks! [[alternative HTML