search for: mlinterfaces

Hi,           I am trying to use MLearn in MLInterfaces package to do randomforest, clustering, knn etc. How do I predict on a test set for which I do not know the classes? My training set has two classes. Thanks, Tulgan __________________________________________________________________ Yahoo! Canada Toolbar: Search from anywhere on the web, a...

..." is not being loaded on my platform. the error message Error in loadNamespace(i[[1L]], c(lib.loc, .libPaths())) : there is no package called 'coin' Error in as.environment(pos) : no item called "newtable" on the search list Error: package/namespace load failed for 'MLInterfaces' In addition there is an error message showing in R which is creating some problem during installaiton I have tried chanign the data directory path using in environment variable(R_LIBS= C:\\Users\\sm\\Documents/R/win-library/2.10)but could not figure out what is wrong with it... this error...

I'm getting this error when I try to run the function MLearn(): Error: could not find function "MLearn" I have the MLInterface tools installed. But when I look for MLearn "??MLearn" but I don't find it. The closest thing I find is the method MLearn_new() under MLInterfaces. I've tried replacing MLearn() with MLearn_new() in my script, but I still get the same error, this time: Error: could not find function "MLearn_new" Any ideas what I'm doing wrong here? Thanks. R version 2.13.4, Biocinstall version 2.8.4, on Ubuntu 10.04 -- View this message...

Hi all, I ran a discriminant analysis with some data and want to get a general idea of prediction error rate. Some have suggested using X-fold cross validation procedure. Anyone know if there is a function for this in R? Thanks, Wade [[alternative HTML version deleted]]

...s and Ripley's MASS matchprobes Tools for sequence matching of probes on arrays Matrix A Matrix package for R methods Formal Methods and Classes mgcv GAMs with GCV smoothness estimation and GAMMs by REML/PQL MLInterfaces Uniform interfaces to R machine learning procedures for data in Bioconductor containers multtest Resampling-based multiple hypothesis testing Neve2006 expression and CGH data on breast cancer cell lines nlme...

I have recently upgraded to Ubuntu Gutsy and, for the first time, am using a 64-bit installation. After failing miserably to install R from source, not a problem for me in the past with a 32-bit install, I went the route of using the Debian Etch build. This went smoothly, but I am unable to update my numerous R and BioConductor packages, getting non-zero exit status errors on each package. Is