similar to: Trouble compiling package on Windows (64 bit)

Dear Professor, I am preparing for a Ph.D in semiparametric regression at Cairo university in Egypt. Referring to R package KernGPLM, I obtained R version 2.4.1 but I did not find package KernGPLM. Please, help me how can I obtain this package. Thanks in advance. Name: Magda Haggag E-mail: magdahaggag@yahoo.com Address: 27, Notrdam Desion st., Gleem, Alexandria, Egypt.

I want to use the caret package and found out about the timingSamps obtion to obtain the time which is needed to predict results. But, as soon as I set a value for this option, the whole model generation fails. Check this example: ------------------------- library(caret) tc=trainControl(method='LGOCV', timingSamps=10) tcWithout=trainControl(method='LGOCV')

Hi, I'm trying to get a package to pass through "R CMD check --as-cran" and have run into a problem that gives me no idea where to look to fix it. I'm running R version 15.1 on a Windows 7 64-bit machine, with the current set of Rtools. An attempt to check the package dies at the following step: * checking running R code from vignettes ... 'SIBER.Rnw'

The following code fails to train a nnet model in a random dataset using caret: nR <- 700 nCol <- 2000 myCtrl <- trainControl(method="cv", number=3, preProcOptions=NULL, classProbs = TRUE, summaryFunction = twoClassSummary) trX <- data.frame(replicate(nR, rnorm(nCol))) trY <- runif(1)*trX[,1]*trX[,2]^2+runif(1)*trX[,3]/trX[,4] trY <-

Hy, I got the following problem when trying to build a rpart model and using everything but LOOCV. Originally, I wanted to used k-fold partitioning, but every partitioning except LOOCV throws the following warning: ---- Warning message: In nominalTrainWorkflow(dat = trainData, info = trainInfo, method = method, : There were missing values in resampled performance measures. ----- Below are some

Dear Help, After loading the pd.Citrus library and checking the DataFrame, I ran > the R code for: > > 1) 'oligo' > > > > {> library(pd.citrus) > Loading required package: RSQLite > Loading required package: DBI > > data(pmSequence) > > > show(pmSequence) > DataFrame with 341730 rows and 2 columns > fid sequence > <integer>

Hello, I'm running some code that requires untaring many files in the first step. This takes a lot of time and I'd like to do this in parallel, if possible. If it's the disk reading speed that is the bottleneck I guess I should not expect an improvement, but perhaps it's the processor. So I want to try this out. I'm working on windows 7 with R 2.15.1 and the latest foreach

I'm using caret to assess classifier performance (and it's great!). However, I've found that my results differ between R2.* and R3.* - reported accuracies are reduced dramatically. I suspect that a code change to kernlab ksvm may be responsible (see version 5.16-24 here: http://cran.r-project.org/web/packages/caret/news.html). I get very different results between caret_5.15-61 +

Hello, I'm training a set of data with Caret package using an elastic net (glmnet). Most of the time train works ok, but when the data set grows in size I get the following error: Error en { : task 1 failed - "arguments imply differing number of rows: 9, 10" and several warnings like this one: 1: In eval(expr, envir, enclos) : model fit failed for Resample01 My call to train