similar to: Environments and parallel processing

This is all normal, a fork cluster works with processes, that do not share memory. When you create a fork cluster, you create a new process, that has the same memory layout as the parent. But from this moment its memory is independent of the parent process. When parLapply is done, the results are serialized and copied back to the parent process. The serialized environment is independent of the

On 12.09.2018 20:20, G?bor Cs?rdi wrote: > This is all normal, a fork cluster works with processes, that do not > share memory. And if you are after shared-memory parallelism, you can try the 'Rdsm' package: https://cran.r-project.org/package=Rdsm Greetings Ralf -- Ralf Stubner Senior Software Engineer / Trainer daqana GmbH Dortustra?e 48 14467 Potsdam T: +49 331 23 61 93 11 F:

(Moved to R-devel) Niek, Would you please provide the details on this test case, including your benchmarks, and what you are trying to achieve at the high-level? Thanks, Michael On Wed, Jun 17, 2015 at 4:55 AM, Niek Bouman <niek.bouman at keygene.com> wrote: > Dear R-core team, > > I was wondering whether you have any plans to improve the current implementation of reference

Dear R developers, I've run into a weird behavior of the numericDeriv function (from the stats package) which I also posted on StackOverflow (question has same title as this email, except for the version of R). Running the code bellow we can see that the numericDeriv function gives an error as the derivative of x^a wrt a is x^a * log(x) and log is not defined for negative numbers. However,

Dear all As far as I could trace, looking at the function C function numeric_deriv, this unwanted behavior comes from the inner most loop in, at the very end of the function, for(i = 0, start = 0; i < LENGTH(theta); i++) { for(j = 0; j < LENGTH(VECTOR_ELT(pars, i)); j++, start += LENGTH(ans)) { SEXP ans_del; double origPar, xx, delta; origPar = REAL(VECTOR_ELT(pars, i))[j];

Hi all, I'm searching for a little clarification on partial mantel tests (ecodist package) I've a distance matrix (x,y), and several others containing environmental/chemical variables. Based on the help file, and the package instructions I've managed to implement the tests as; var1 ~ env1 + space to partial out the effect of space and test the relationship between the

Hi, I am running a logistic regression model using lrm library and I get the following error when I run the command: mod1 <- lrm(death ~ factor(score), x=T, y=T, data = env1) Unable to fit model using ?lrm.fit? where score is a numeric variable from 0 to 6. LRM executes fine for the following commands: mod1 <- lrm(death ~ score, x=T, y=T, data = env1) mod1<- lrm(death ~

Hello, I'm trying to illustrate the relationships between various trait and environment data gathered from a number of sites. I've created a GAM to do this: gam1=gam(trait~s(env1)+s(env2)+te(env1,env2)) and I know how to create a 3D plot using vis.gam. I want to be able to show points on the 3D plot indicating the sites that the data came from. I can do this on a 2D plot when there is one

Hi, Currently object.size() is not very useful on environments as it always returns 56 bytes, no matter how big the environment is: env1 <- new.env() object.size(env1) # 56 bytes env2 <- new.env(hash=TRUE, size=75000000L) object.size(env2) # 56 bytes env3 <- list2env(list(a=runif(25000000), L=LETTERS)) object.size(env3) # 56 bytes This makes it pretty useless on

Hi Gabe, On 09/29/2015 02:51 PM, Gabriel Becker wrote: > Herve, > > The problem then would be that for A a refClass whose fields take up N > bytes (in the sense that you mean), if we do > > B <- A > > A and B would look like the BOTH take up N bytes, for a total of 2N, > whereas AFAIK R would only be using ~ N + 2*56 bytes, right? Yes, but that's still a *much*

I am running into a problem with my LDAP puppet setup with this error message: PuppetClient: err: Could not retrieve catalog from remote server: Error 400 on SERVER: Could not parse for environment DR: undefined method `to_sym'' for ["ENV1", "ENV2"]:Array PuppetMaster: err: Could not parse for environment DR: undefined method `to_sym'' for ["ENV1",

Hi, I have a problem with running puppet with hiera based data. I am trying to cleanup user accounts on a range of servers (to have the same uid/gid). When I create existing account but with different uid/gid it doesn''t change files group ownership so I need to run a script which cleanups after cleanup ;-) I use the following code: --- :backends: - yaml :hierarchy: -

Hello, I''m using the following expression to format a list: $valid_environments = [''env1'', ''env2'', ''env3''] $env_message = $valid_environments.reduce |$message, $env| { "${message}, ${env}" } It works at run-time (Puppet 3.2.4 standalone with "--parser=future"). However in Eclipse (v4.3.1),

Thanks; definitely a bug. I've submitted it to the bug tracker at https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17831 Best, luke On Mon, 15 Jun 2020, Raimundo Neto wrote: > Dear R developers, > > I've run into a weird behavior of the numericDeriv function (from the stats > package) which I also posted on StackOverflow (question has same title as > this email,

Hi R Users, I created interaction plots in ggplot2 and was trying to add output of two way ANOVA models, especially only interaction ( example treatment*control F(XX, XX) = xxx, p = xxx) into figures, but i was not able to add. Would you mind to help on how I can add information into each figure? I have attached the example data and the code that I used for this. dat<-structure(list(Sites

Hi R-developers In the package Parallel, the function parLapply(cl, x, f) seems to allow transmission of only one parameter (x) to the function f. Hence in order to compute f(x, y) parallelly, I had to define f(x, y) as f(x) and tried to access y within the function, whereas y was defined outside of f(x). Script: library(parallel) f <- function(x) { z <- 2 * x + .GlobalEnv$y # Try to

Thank you for your answer! I agree with you except for the 3 (Error) example and I realize now I should have started with that in the explanation. >From my point of view parLapply(cl = clu, X = 1:2, fun = fun, c = 1) shouldn't give an error. This could be easily avoided by using all the argument names in the custerApply call of parLapply which means changing, parLapply <-

Dear R-user, I am trying to use the function 'parLapply' from the 'snow' package which is supposed to work the same wys as 'lapply' but for a parallelized cluster of computers. The function I am trying to call in parallel is 'dudi.pca' (from the 'ade4' package) which performs principal component analyses. When I call this function on a list of

Hi! I recognized that the argument matching of clusterApply (and therefore parLapply) goes wrong when one of the arguments of the function is called "c". In this case, the argument "c" is used as cluster and the functions give the following error message "Error in checkCluster(cl) : not a valid cluster". Of course, "c" is for many reasons an unfortunate

Hello everybody, I've got a bit of a problem with parLapply that's left me scratching my head today. I've tried this in R 2.11 and the 23 bit Revolution R Enterprise and gotten the same result, OS in question is Windows XP, the package involved is the snow package. I've got a list of 20 rain/no rain (1/0) situations for these two stations i and j, all the items in this list look