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.../test_source.R’, print.eval=TRUE) The summary information and dotplot printed just fine. Dan Daniel J. Nordlund Washington State Department of Social and Health Services Planning, Performance, and Accountability Research and Data Analysis Division Olympia, WA 98504-5204 From: James Jong [mailto:ribonucleico@gmail.com] Sent: Thursday, February 07, 2013 1:48 PM To: Nordlund, Dan (DSHS/RDA) Subject: Re: [R] Sourcing my file does not print command outputs Thanks Dan, Here is a minimal full example, where dotplot fails to print anything if I source the file. I have tried restarting R and directly sourcin...

I looked at the documentation of source() and summary(), and I could not find the reason why calling something like: > summary(resamps) from the command line, works (it prints the summary) whereas calling summary(resampls) from a file that I source with source("my_file.r") does not print anything. How can I get summary(resamps) to print when I source a file with this command?

Hi - I am having trouble installing rpy2. I have already compiled R as a shared library, but I do not have admin privileges on so I am trying to install rpy2 with: pip install -user rpy2 but I am getting the following error: ======================================== ./rpy/rinterface/_rinterface.c:86:31: fatal error: readline/readline.h: No such file or directory compilation terminated. error:

I have a binary classification problem where the fraction of positives is very low, e.g. 20 positives in 10,000 examples (0.2%) What is an appropriate cross validation scheme for training a classifier with very few positives? I currently have the following setup: ======================================== library(caret) tmp <- createDataPartition(Y, p = 9/10, times = 3, list = TRUE)

The documentation for caret::train shows a list of parameters that one can tune for each method classification/regression method. For example, for the method randomForest one can tune mtry in the call to train. But the function call to train random forests in the original package has many other parameters, e.g. sampsize, maxnodes, etc. Is there **any** way to access these parameters using train

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 <-

I have an environment variable `$R_HISTFILE` pointing to `/home/my.username/.RHistory` and the following code in my `.Rprofile` in my home directory: .Last <- function() { if (!any(commandArgs()=='--no-readline') && interactive()){ require(utils) try(savehistory(Sys.getenv("R_HISTFILE"))) } }

Say I train a model in caret, e.g.: RFmodel <- train(X,Y,method='rf',trControl=myCtrl,tuneLength=1) How can I save this to disk and load it later in R? How about an object of the class "resamples"? resamps <- resamples( list( RF = RFmodel, SVM = SVMmodel, KNN = KNNmodel, NN = NNmodel )) Thanks,

What is the purpose of the variable R_HOME? What value should I set it to? Thanks, James [[alternative HTML version deleted]]

What is the function call interface for predict in the package party for cforest? I am looking at the documentation (the vignette) and ?cforest and from the examples I see that one can call the function predict on a cforest classifier. The method predict seems to be a method of the class RandomForest objects of which are returned by cforest. --------------------------- > cf.model =

I have carefully read the CARET documentation at: http://caret.r-forge.r-project.org/training.html, the vignettes, and everything is quite clear (the examples on the website help a lot!), but I am still a confused about the relationship between two arguments to trainControl: "method" "index" and the interplay between trainControl and the data splitting functions in caret

When I try to crate a grid of parameters for training with caret I get various errors: ------------------------------------------------------------ > my_grid <- createGrid("rf") Error in if (p <= len) { : argument is of length zero > my_grid <- createGrid("rf", 4) Error in if (p <= len) { : argument is of length zero > my_grid <-