similar to: Question about R performance on UNIX/LINUX with different memory/swap configurations

Is this a SPARC system? On at least some SPARC systems, the "long double" type in C is implemented very slowly in software, and it seems that it is used for the sums done when calculating standard deviations with "sd". Radford Neal > Date: Wed, 8 Aug 2012 18:55:37 -0500 > From: "Eberle, Anthony" <aeber at allstate.com> > To: <r-devel at

I have a question about multiple cores and CPU's for running R. I've been running various tests on different types of hardware and operating systems (64 bit, 32 bit, Solaris, Linux, Windows, RV.10, .12, .15, .15.1.) Generally speaking, it seems that for a single user and process that R prefers to have as much resources as possible; especially memory. I've looked at some of the r-sig

I have a question about how one can modify or override the compilers that R uses for package installations? Or if perhaps this configuration is in some editable file somewhere. Initially I built the version of R 2.15.1 on Solaris SPARC (virtual T4), but found out the build was done as 32 bit. After some research, I found that the pre-compiled GCC version I had only allowed for 32 bit. I wanted

Hi, although I've done S3 dispatching for more than a decade now, I think I managed to overlook/avoid the following pitfall when using NextMethod(): If you explicitly pass argument '...' to NextMethod(), you will effectively pass those argument twice to the "next" method! EXAMPLE: foo0 <- function(...) UseMethod("foo0"); foo1 <- function(...)

All -- I have been trying to work with the 'Party' package using R v2.15.1 and have cobbled together a (somewhat) functioning code from examples on the web. I need to run a series of unbiased, conditional, cForest tests on several subsets of data which I have made into a loop. The results ideally will be saved to an output file in matrix form. The two questions regarding the script in

Dear expeRts, is it possible to carry out calculations between different foreach() calls? As for nested loops, you want to carry out calcuations not depending on the inner loop only once and not for each iteration of the innermost loop. Cheers, Marius library(foreach) foreach(i=1:3) %:% foreach(j=1:2) %do% { i <- i+1 print(paste(i,j)) } foreach(i=1:3) %:% i

This is a general question to people who've installed R on a UNIX sparc-sun-solaris platform: Have you had any issues related to maintaining R on this platform, e.g., installations that didn't work, instances of R crashing and possibly requiring a new installation, etc? I'm especially interested in anyone who has experience with both R and S-Plus on this OS. Is there any reason to

Good afternoon Has anyone tried using Dr. Elith's BRT script? I cannot seem to run gbm.step from the installed gbm package. Is it something external to gbm? When I run the script itself <- gbm.step(data=model.data, gbm.x = colx:coly, gbm.y = colz, family = "bernoulli", tree.complexity = 5, learning.rate = 0.01, bag.fraction = 0.5) ... I

Dear gbm users, When running predict.gbm() on a "large" dataset (150,000 rows, 300 columns, 500 trees), I notice that the memory used by R grows beyond reasonable limits. My 14GB of RAM are often not sufficient. I am interpreting this as a memory leak since there should be no reason to expand memory needs once the data are loaded and passed to predict.gbm() ? Running R version 2.9.2 on

Dear all, the help file for predict.gbm states that "The predictions from gbm do not include the offset term. The user may add the value of the offset to the predicted value if desired." I am just not sure how exactly, especially for a Poisson model, where I believe the offset is multiplicative ? For example: library(MASS) fit1 <- glm(Claims ~ District + Group + Age +

I am trying to call a C++ k-means function within R and I am struggling. I know that the below code is used to call a C++ function for gbm but how do I do it for k-means? gbm.obj <- .Call("gbm", Y=as.double(y), Offset=as.double(offset), X=as.double(x), X.order=as.integer(x.order),

HI, Dear Greg, I AM A NEW to GBM package. Can boosting decision tree be implemented in 'gbm' package? Or 'gbm' can only be used for regression? IF can, DO I need to combine the rpart and gbm command? Thanks so much! -- Sincerely, Changbin -- [[alternative HTML version deleted]]

Hi, I have a geforce 4mx 440 agp 8x, and I'm trying to use the GBM library, (as jbarnes in: http://virtuousgeek.org/blog/index.php/jbarnes/2011/10/ and David Hermann in KMSCON: https://github.com/dvdhrm/kmscon), without success. when I try to create a gbm_device, I get: (below the code.) nouveau_drm_screen_create: unknown chipset nv18 dri_init_screen_helper: failed to create pipe_screen

I recently use gbm for a binary classification problem. As expected, it gets very good results, based on Area under ROC with 7-fold cross validation. However, the application (malware detection) is cost-sensitive, getting a FP (classify a clean sample as a dirty one) is much worse than getting a FN (miss a dirty sample). I would like to tune the gbm model biased to very low FP rate. For this

Hi, there: I am wondering if I can find some detailed explanation on gbm or explanation on examples of gbm. thanks, Ed

Hi, I am having problems passing arguments to method="gbm" using the train() function. I would like to train gbm using the laplace distribution or the quantile distribution. here is the code I used and the error: gbm.test <- train(x.enet, y.matrix[,7], method="gbm", distribution=list(name="quantile",alpha=0.5), verbose=FALSE,

Dear All, I am far from being a guru about parallel programming. Most of the time, I rely or randomForest for data mining large datasets. I would like to give a try also to the gradient boosted methods in GBM, but I have a need for parallelization. I normally rely on gbm.fit for speed reasons, and I usually call it this way gbm_model <- gbm.fit(trainRF,prices_train, offset = NULL, misc =

Hola de nuevo. Se me olvidaba la principal razón para utilizar gbm.step del paquete dismo. Como sabéis, los boosted si sobreajustan (a diferencia de los random forest o cualquier otro bootstrap) pero gbm.step hace validación cruzada para determinar el nº óptimo de árboles y evitarlo. Es fundamental. La opción que me queda, Carlos, es hacerlo con gbm, pero muchas veces, y usar el

I color some area grey with polygon() (with a red border) and then I want to have the dashed red border in the legend as well. How do I manage it? And I want to mix (latex) expressions with text in my legend. Just execute my lines below and you know want I mean. Or pass by at http://de.wikipedia.org/wiki/Bild:GBM.png to see the picture online. Thomas bm <- function(n=500, from=0, to=1) {

Full_Name: The Manager Version: 2.0.1 OS: Solaris 9 Submission from: (NULL) (129.67.80.243) > install.packages("gbm") trying URL `http://cran.uk.r-project.org/src/contrib/PACKAGES' Content type `text/plain; charset=ISO-8859-1' length 52975 bytes opened URL ================================================== downloaded 51Kb trying URL