similar to: SVM

Hello, if we suppose that times <- c("2006-05-14", "2006-06-12", "2006-06-12", "2006-05-14", "2006-05-14", "2006-06-12") value <- c(2,3,1,4,3,1) then with plot(times, value) we have two boxplots in one graph for 2006-05-14 and 2006-06-12 respectively! Is it possible to have them in a scatterplot? and

Hello, I have got the following problem: > setwd("C:/temp") > library(xlsReadWrite) > MyData <- read.xls(file="Mappe1.xls", colNames = TRUE,dateTimeAs = "isodatetime") > attach(MyData) > MyData name value times 1 A1 2 2006-05-12 2 A2 3 2006-05-16 3 A3 1 2006-05-12 4 A4 4 2006-05-12 5 A5 2

Hello, I am trying to read the SAS file MyData.sa7bdat in R! This file is saved under D:\data! I therefore wrote > path <-"D:/SasData" > sashome <- "C/Progra, Files/SAS Institute/9_1/SAS" > sascmd <- file.path(sashome, "sas.exe") > MyData <- read.ssd(path, "MyData", sascmd=sascmd) The results what I get:

Hello, I am interested in Figure 2 in http://www-stat.stanford.edu/~tibs/ftp/cus.pdf Can anyone tell please how to create this plot? Many thanks Samuel [[alternative HTML version deleted]]

Hi, I have the following data data1 <- data.frame(count = c(0,1,1,2,4,5,13,16,14), weeks = 1:9,                     treat=c(rep("1mg",3),rep("5mg",3),rep("10mg",3))) and I am using library(splines) to fit glm.m <- glm(count~bs(weeks)+as.factor(treat),family=poisson,data=data1) and I am interested in predicting the count variale for the weeks 10, 11 and

Hello, suppose one has the following values x1 <- rnorm(10,5,1) x2 <- rgamma(10,5,1) y <- rnorm(10,4,1) mydat <- data.frame(y,x1,x2) then one can use glm like mod <- glm(y~x1+x2, data=mydat, family=gaussian) But how could I estimate y_hat? Thanks alot! Sam --------------------------------- [[alternative HTML version deleted]]

Hallo, once again I have a question. Maybe someone can help me. I call in my programm (VC++) the Rterm.exe via "Rterm.exe --no-restore --no-save < example.R >example.Rout". In example.R : temperaturfeld <- read.table(file.choose(), header = TRUE, sep= "", comment.char = "#") temperaturmatrix <- data.matrix(temperaturfeld) windows()

Clicking on the icon of a clusterer opens an infowindow with a list of markers , ech one has an icon and a description... Is it possible to click on the marker icon (how to change its image ?) to zoom directly to that marker ? or should I write a link in the description yhanks for your help Note :additional question : what is the origin of the error message : '' too many

Y have only continuos quantitative data and when I trie the function I get next message > res<-clues(C,quiet=TRUE) Error en ChooseK_sil(y, y2 = y, n0, alpha, eps, itmax, second, K2.vec, : el objeto (list) no puede ser coercionado a 'double' Last line would be the object could (list) not be coerced to ´double´ What can be the problem? Yhanks

Hey! How do I get the confusion matrix after performing 10-fold cross validation from SVM in R? When I try to print it, I get the confusion matrix without cross validation. I need to compute PPV. Should I report PPV without CV and total accuracy with CV? I am confused. > svmtrain <- svm(xtrain,ytrain,kernel="sigmoid",cross=10) > pred <- predict(svmtrain, xtrain) >

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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

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>

svm.fit<-svm(as.factor(out) ~ ., data=all_h, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) # model fitting svm.pred<-predict(svm.fit, hh, decision.values = TRUE, probability = TRUE) # find the probability, but can not find. attr(svm.pred, "probabilities") > attr(svm.pred, "probabilities") 1 0 1 0 0 2 0

> x <- train[,c( 2:18, 20:21, 24, 27:31)] > y <- train$out > > svm.pr <- svm(x, y, probability = TRUE, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) > > pred <- predict(svm.pr, valid[,c( 2:18, 20:21, 24, 27:31)], decision.values = TRUE, probability = TRUE) > attr(pred, "decision.values")[1:4,]

I am trying to understand what is the difference between linear and polynomial kernel: linear: u'*v polynomial: (gamma*u'*v + coef0)^degree It would seem that polynomial kernel with gamma = 1; coef0 = 0 and degree = 1 should be identical to linear kernel, however it gives me significantly different results for very simple data set, with linear kernel

Hi, I have a question about the parameter C (cost) in svm function in e1071. I thought larger C is prone to overfitting than smaller C, and hence leads to more support vectors. However, using the Wisconsin breast cancer example on the link: http://planatscher.net/svmtut/svmtut.html I found that the largest cost have fewest support vectors, which is contrary to what I think. please see the scripts

Dear All , 1- I'm trying to access the values of fitted(model) after model<- tune.svm( ) but seemingly it is not poosible. How can I access to values of fitted ? However ,it is possible only after model<- svm( ) 2- How can I access to the other values such as the number of Support Vectors , gamma, cost , nu , epsilon , after model<- tune.svm( ) ? these are not possible? I

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

*Hi, *Does anyone know how can I show an *ROC curve for R-SVM*? I understand in R-SVM we are not optimizing over SVM cost parameter. Any example ROC for R-SVM code or guidance can be really useful. Thanks, Angel. [[alternative HTML version deleted]]