similar to: Multicore/Parallel

I am trying to do parallel programming and I tried this library(doSNOW) library(foreach) testfunc<-function(x){ x<-x+1 x } noc<-2 cl <- makeCluster(do.call(rbind,rep(list("localhost"),noc)), type = "SOCK") registerDoSNOW(cl) clusterExport(cl=cl,c("testfunc.r")) testl<-foreach(pp=1:2) %dopar% { testfunc(pp) } And this works but if I try to

Is there a way to extract list of words in BioWordVec in R Thank you Tjun Kiat [[alternative HTML version deleted]]

I *think* this might be better posted here: https://bioconductor.org/help/support/ Cheers, Bert On Thu, Feb 1, 2024 at 4:37?PM TJUN KIAT TEO <teotjunk at hotmail.com> wrote: > Is there a way to extract list of words in BioWordVec in R > > Thank you > > Tjun Kiat > > [[alternative HTML version deleted]] > > ______________________________________________

I am trying to use the package ode and periodically it will come up with this error message Warning..Internal T (=R1) and H (=R2) are such that in the machine, T + H = T on the next step (H = step size). Solver will continue anyway. And then the program just take very long to run. Is there anyway to get the program to terminate when this warning is issued instead of continuing to run ?

This is applicable to either using optim or the differential equation solver or any similar solver Suppose I want to use the differential equation solver and this is my code d<-y[2] vdpol<-function(t,y) { list(c(1, d, 3, 4 ) } stiff<-ode(y=rep(0,4),times=c(0,1),func=dvdpol,parms=1) The thing is I want d to be composed of one of state variables in the

I am trying to to write a wrapper function for the ode solver (under the package desolve) to enable it to take multivariate arrays. I know how to do it for 1 dimension arrays but my code breaks down when I try to do it for 2 dimensional arrays. Here is my code diffwrap<-function(t,y,mu)vdpol(t=t,A[1:3,1:4]<-y[1:12],B[1:12]<-y[13:24],mu=mu) vdpol<-function(t,A,B,mu) { list(c(mu,

How would I rewrite my code so that I can implement the use of multicore on an Rstudio server to run regsubsets using the "exhaustive" method? The data has 1200 variables and 9000 obs so the code has been shortened here: model<-regsubsets(price~x + y + z + a + b + ...., data=sample, nvmax=500, method=c("exhaustive")) Our server is a quad core 7.5 gb ram, is that

Dear List, I have a set of data which looks like this (small set of sample) A A 0.431 A A 0.439 A A 0.507 A G 0.508 A A 0.514 I will like to use this data to plot a dot plot, with the X-axis being of type character, and my y axis of type numeric. When I try to use the dot chart function, I get the error message "'x' must be a numeric vector or matrix", which I can understand

Dear All, I hope I am not asking a FAQ. I am dealing with a problem of graph theory [connected components in a non-directed graph] and I do not want to rediscover the wheel. I saw a large number of R packages dealing for instance with the k-means method or hierarchical clustering for spatially distributed data and I am basically facing a similar problem. I am given a set of data which are the

HiI want to construct a logliikelood function in RHere is the situationy=number of particles emitted in 1 hr period~pois(30)p=probability of detection of radiation particlesx=number of particles detected by a radiation detector~pois(30p)where p~beta(a,1)I have to calculate the loglikehood for a for the range a(2,50)I wish to simulate 100 random samples for each aHere is my code:-m=481n=100x =

Dear list, I am just starting on analysis of count data in R 3.4.0. My dataset was obtained from counting particles on a surface before andd after a cleaning process. The sampling positions on the surface are pre-defined and are the same before and after cleaning. I have ~20% of 0's. I want to know if the cleaning process was useful at reducing the number of particles. I first fit a

Hello All, In general when we use Rprof for performance evaluation on Multicore systems the output provides the time on the basis of the "user" time and the sampling time is equal to the the user time as reported by system.time. This does not seem right behavior when R is linked to BLAS/Lapack or other libraries which are optimized for parallel or multicore architectures as

Hello All, In general when we use Rprof for performance evaluation on Multicore systems the output provides the time on the basis of the "user" time and the sampling time is equal to the the user time as reported by system.time. This does not seem right behavior when R is linked to BLAS/Lapack or other libraries which are optimized for parallel or multicore architectures as

Hello veryone, I am interested in the diffusion of particles inside a sphere, and its release through a small pore on the sphere surface. Unfortunately, I have not found the way to do this in R. Could you help me? Thank very much in advance for your help

Hi, I would like to get suggestion about parallel computing package on a multicore windows workstation. I tried doSMP, but it crashes R a lot. I am wondering if "snow" and "snowfall" can be used on a single workstation (i,e, not cluster). At suggestion would be appreciated, Best, Richard [[alternative HTML version deleted]]

Hi everyone, Thanks for the help in advance. I just want to know if there is absolutely any way of using multicore if my loop can't run in parallel as each iteration depends on the previous iteration. I have Windows R 2.9.2 and REvolution R Enterprise 2.0 for Windows 64 bit. I also have if necessary R on Ubuntu. 8gb of ram and a core 2 duo processor but I could use a quad core as well. Many

Looks like people send many names in the contest, so shall I do too. I propose "Lepton", loosely meaning light (featherweight) in Greek. It is used in particle physics to refer to very light particles (electrons, muons). For me it also has the connotations of fastness and restlessness (in the LLVM sense of post-compile optimization). See more at:

People of R(th), I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to parallelize a bootstrap of a linear model on my 8-core mac. Below is the process that I want to parallelize (namely, the m2.ph.rlm.boot<-boot(m2.ph,m2.ph.fun, R = nboot) command). This is an extension of the bootstrapping linear models example in Venables and Ripley to

> On Oct 2, 2017, at 2:05 AM, David <dasolexa at hotmail.com> wrote: > > Dear list, > > > I am just starting on analysis of count data in R 3.4.0. My dataset was obtained from counting particles on a surface before andd after a cleaning process. The sampling positions on the surface are pre-defined and are the same before and after cleaning. I have ~20% of 0's. I

On Mon, 2016-02-08 at 14:22 -0800, John R Pierce wrote: > the only truly safe way to destroy data on magnetic media is to grind > the media up into filings or melt it down in a furnace. I unscrew the casing, extract the disk platter(s), slide a very strong magnet over both sides of the platter surface then bend the platter in half. How secure is that ? I can't afford a machine that