search for: perdisci

Hello R list, I have a little problem with splom. I'd like to wrap it in a function, for example: multi.scatterplot <- function(data,groups,cols,colors) { splom(~data[,cols], groups = as.symbol(groups), data = data, panel = panel.superpose, col=colors) } and then call it like in multi.scatterplot(iris,"Species",1:4,c("green","blue","red"))

...kage called 'RGtk2' in: library(package, lib.loc = lib.loc, character.only = TRUE, logical = TRUE, > local({pkg <- select.list(sort(.packages(all.available = TRUE))) + if(nchar(pkg)) library(pkg, character.only=TRUE)}) > update.packages(ask='graphics') On 2/28/07, Roberto Perdisci <roberto.perdisci@gmail.com> wrote: > > Hi, > out of curiosity, what is the name of the package you found? > > Roberto > > On 2/27/07, j.joshua thomas <researchjj@gmail.com> wrote: > > Dear Group, > > > > I have found the package. > > > &g...

Hello, I'd need to compute the calibration error of posterior class probabilities p(y|x) estimated by using rpart as classification tree. Namely, I train rpart on a dataset D and then use predict(... type="prob") to estimate p(y|x). I've found the possibility to do that in the ROCR package, but I cannot find a link to a paper/book which explains the details of the

Hello everybody, after searching around for quite some time, I haven't been able to find a package that provides a function to compute the Windorized mean and variance. Also I haven't found a function that computes the trimmed variance. Is there any such package around? thanks, Roberto

Hi everybody, I noticed a strange behavior when using loops versus apply() on a data frame. The example below "explicitly" computes a distance matrix given a dataset. When the dataset is a matrix, everything works fine. But when the dataset is a data.frame, the dist.for function written using nested loops will take a lot longer than the dist.apply ######## USING FOR ####### dist.for

...to the performance measure 'auc', allowing to calculate the partial area under the ROC curve up to the false positive rate given by 'fpr.stop'. - fixed bug in 'prediction' function which caused ROCR to halt in the context of a custom label.ordering (thanks to Roberto Perdisci for pointing out) As usual, any feedback is more than welcome! - Tobias -- Tobias Sing Computational Biology and Applied Algorithmics Max Planck Institute for Informatics Saarbrucken, Germany Phone: +49 681 9325 315 Fax: +49 681 9325 399 http://www.tobiassing.net ____________________________...

...to the performance measure 'auc', allowing to calculate the partial area under the ROC curve up to the false positive rate given by 'fpr.stop'. - fixed bug in 'prediction' function which caused ROCR to halt in the context of a custom label.ordering (thanks to Roberto Perdisci for pointing out) As usual, any feedback is more than welcome! - Tobias -- Tobias Sing Computational Biology and Applied Algorithmics Max Planck Institute for Informatics Saarbrucken, Germany Phone: +49 681 9325 315 Fax: +49 681 9325 399 http://www.tobiassing.net ____________________________...

Hello, I'm new using R. I'm trying to develop a K-means Clustering with R for some data I have, however each time I use that instruction with the same data my cluster means, clustering vector and within cluster sum of square change and I don't understand why because I use the same parameters and the same data. Can anybody explain me why does it happen? Thank you Act. Calef

Hello, I'm trying to using ksvm() in the kernlab package to fit a one-class SVC, but I get a strage result on the cross-validation error estimate. For example, consider this code: data(spam) classifier <- ksvm(type~.,data=spam[which(spam[,'type']=='spam'),], type="one-svc",kernel="rbfdot",kpar=list(sigma=0.1),nu=0.05,cross=10) what I get is: >