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Greetings, I am trying to use Reference Class methods in Snowfall, using R 2.12.1 on Ubuntu Natty. Using then directly seems to work (stanza 2 below), but using them indirectly does not (stanza 3 below). I get an "attempt to apply non-function" error. In addition to exporting the instance of the object I created to the Snowfall slaves, I also made several attempts to export the

Sorry for wasting your time with an incorrect diagnosis in my previous mail! I'm running my script using mpirun -mp 4 and using snowfall+Rmpi on Linux 64bits. I receive the following error, that terminates the run. Any idea? I'm able to reproduce the problem with a minimal script (below). If n < 16371 it completes 9 iterations without problems. Already at 16371 it dies at iteration 7

I'm running my script using mpirun -mp 4 and using snowfall+Rmpi on Linux 64bits. I receive the following error, but apparently without consequences on the results. Any idea? I'm able to reproduce it with a minimal script (below). Seems the critical issue is the for loop. Without it no error. Thanks for your help! TERM: Undefined variable. TERM: Undefined variable. TERM: Undefined

  Hi,   The code below does exactly what I want in sequential mode. But, it is slow and I want to run it in parallel mode. I examined some windows version packages (parallel, snow, snowfall,..) but could not solve my specific problem. As far as I understood, either I have to write a new function like sfRollapplyr or I have to change my code in a way that it utilizes lapply, or sapply instead of

...39;, cc = "catt")) f1 <- function(y1) { new('catt', aa = y1 + 1) } f3 <- function(y2, y3) { new('dogg', bb=c(y2, 'GA'), cc = f1(y3)) } dat <- 1:5 f1(dat) f3('NY', dat) sapply(c("state1", 'state2', 'state3'), f3, dat) sfExportAll() sfClusterEval(ls()) sfSapply(c("s1", 's2', 's3'), f3, dat) sfStop() # sfSapply generates the following error # Error in checkForRemoteErrors(val) : # 2 nodes produced errors; first error: "dogg" is not a defined class === end ============= -- View...

R-helpers: What would be the absolute fastest way to make a large "empty" file (e.g. filled with all zeroes) on disk, given a byte size and a given number number of empty values. I know I can use writeBin, but the "object" in this case may be far too large to store in main memory. I'm asking because I'm going to use this file in conjunction with mmap to do parallel

Hola: Hace como un mes pregunte si se podía desarrollar una barra de progreso que informara del progreso de una subida de datos a SQL Server desde R. Miguel Ángel me dio una orientacion de como enfocar el problema y al final lo he conseguido hacer usando el paquete snowfall. Ahora bien hay una parte del código que, aunque funciona no entiendo muy bien como, porque "adapte" un código

I have data that looks like this: > df1 group id 1 red A 2 red B 3 red C 4 blue D 5 blue E 6 blue F I want a list of the groups containing vectors with the ids. I am avoiding subset(), as it is only recommended for interactive use. Here's what I have so far: df1 <- data.frame(group=c("red", "red", "red", "blue",