similar to: Questions bout SVM

Yet another question... I'm wondering if there is a built in facility to log errors. I've got this statement that gives me verbose DBI errors as they come up (to standard output), but I'd like to trap and log them to a file as I running about 3000000 sql statements through this particular piece of code and I'd like to keep the loop going and deal with all the errors once the

Hi everyone, Can anyone please tell whether there is a difference between the code for using svm in regression and code for using svm in classification? This is my code for regression, should I change it to do classification?: train <- read.table("trainingset.txt",sep=";") test <- read.table("testset.txt",sep=";") svmmodelfitness <-

Hi, Could you please tell me how I correct the following error message? “Error in object$tables[[v]] : subscript out of bounds” This is the code: library(e1071) data(iris) attach(iris) class_label <- names(iris)[1] myformula <- formula(paste(class_label,"~ .")) mymodel<-naiveBayes(myformula, iris,cross=3) predict(mymodel,iris) ##Error in object$tables[[v]] :

Philipp Benner reported a Debian bug report against r-cran-rpart aka rpart. In short, the issue has to do with how rpart evaluates a formula and supporting arguments, in particular 'weights'. A simple contrived example is ----------------------------------------------------------------------------- library(rpart) ## using data from help(rpart), set up simple example myformula <-

Hi, I am passing a data frame and field name to a function. I've figured out how I can create the formula based on the passed in field name, but I'm struggling to create a vector based in that field. for example if I hard code with the actual field name Y = df$Target, everything works fine. but if I use the passed in parameter name, it doesn't give me what I want, Y =

Dear All, I have a question about formulas and model.matrix(). If one specifies a model via a formula, the corresponding design matrix can be obtained with the model.matrix() function. For example: x1 <- c(1,4,2,3,5) x2 <- c(1,1,2,2,2) myformula <- ~ x1 + factor(x2) model.matrix(myformula) My question is: If an intercept term is in the model (like in the example above), is it always

Hi, I am passing a data frame and field name to a function. I've figured out how I can create the formula based on the passed in field name, but I'm struggling to create a vector based in that field. for example if I hard code with the actual field name Y = df$Target, everything works fine. but if I use the passed in parameter name, it doesn't give me what I want, Y = df$mytarget

Hello all. I have this code: myLOOCV <- function(myformula, data) { Y <- all.vars(myformula)[1] Scores<- numeric(length(data[,1])) for (i in 1:length(data[,1])) { train <- data[-i,] test <- data[i,] myModel <- lrm(myformula, train) Scores[i] <- predict(myModel, test,type="mean") }

In S3 if you are passing an evaluated formula to another function that will store it and use it in printing (such as a model-fitting function), you usually want to unclass the formula. Otherwise, the formula that gets stored looks very ugly when printed. A trick to do the unclassing is to pass form = c( myFormula ) In R this has the effect of making form a list rather than a call. It seems

Hello! I am trying to create a formula object using as.formula. But it's not working: examplephraze<-"for.my.example" myformula<-as.formula(paste(examplephraze,"~group, sum, data=mydata",sep="")) What's the problem? Thanks a lot! -- Dimitri Liakhovitski Ninah Consulting www.ninah.com

Hi all, I am currently building an application based on R 1.7.1 (+ compiled C/C++ code + MySql + VB). I am building this application to work on 2 different platforms (Windows XP Professional (500mb memory) and Windows NT 4.0 with service pack 6 (1gb memory)). This is a very memory intensive application performing sophisticated operations on "large" matrices (typically 5000x1500

I am wanting to create a model matrix and keep the NAs. stratmat <- model.matrix(myformula,mydata) Is there any way to do this? model.matrix doesn't have na.action as a parameter. Elsewhere I have made use of na.keep <- function(x){x}. Many thanks, Rachel Cunliffe -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi, Could you please help me in solving the following error message: Error in `[.data.frame`(mytestdata, fp_temp == 1) : undefined columns selected when I use scan instead on read.table, I reicieve this message: Error in names(ret2) <- rowns : 'names' attribute [172] must be the same length as the vector [152] Many thanks, Nancy

Thank you Frank and all for your advices. Here I attach the raw data from the Pawinski's paper. I have obtained permission from the corresponding Author to post it here for everyone. The only condition of use is that the Authors retain ownership of the data, and any publication resulting from these data must be managed by them. The dataset is composed as follows: patient number / MMF dose in

Hi - I use stepAIC to automatically select the model. The stepAIC was applied on polr as follow:objPolr <- polr(formula=myformula, data=dat, method=METHOD);objPolr.step <- stepAIC(objPolr, trace=T);Then R complaints that it doesn't know about 'dat' when it executes the second line. Below is the exact error that I got when executing the stepAIC line above:Error in eval(expr,

Dear Researchers, someone know the right syntax to chose a 5% significance level (threshold) for the inclusion of the model variables in a multiple (linear) regression in backward way? I set the formula in this way, but I don't know to choose the 5% significance? lmodelV <- step(lm(formula=MyFormula[[1]],data=LR.train),direction="backward") thanks in advance gianni

Hi there, I am trying to export a regression output to Latex. I am using the xtable function in the xtable library. Doing myfit <- lm(myformula, mydata) print.xtable(xtable(myfit), file="myfile") only returns the estimated coefficients and the correspondent standard erros, t-statiscs and p-values. But I wish to get a bit more, say, the number of observations used in the

Hi, I am a graduate student applying published R scripts to compare the classification accuracy of 2 predictive models, one built using discriminant function analysis and one using random forests (webpage link for these scripts is provided below). The purpose of these models is to predict the biotic integrity of streams. Specifically, I am trying to compare the classification accuracy (i.e.,

I'm new in both R and statistics. I "did my homework", I tried the archives and whatever I managed to get from the sources, but still I need assistance with the plsr package. I have a model with 2 core determinants D1 and D2, made by 3 indicators each (D1a,D1b,D1c and so on). Also I have 2 moderating variables (m1,m2), where m1 moderates D1 and m2 modarates D2. The dependent

I''m new to rspec and looking for way to test a validation I added to a model. The test checks to see that if field1 has a value then field2 must be nil and vice versa. ------------------------------- When I did the rspec_scaffold it generated one test which worked before :each do @valid_attributes = { :field1 = "value for field1" :field2 = "value for