similar to: Parallel R

... and the mailing list is picky about attachments... whatever you attached did not conform to the stringent requirements mentioned in the Posting Guide. Pasting the code right into the email is usually safest, though you DO have to post using plain text (as the Posting Guide indicates) or your code may get mangled by the automatic html format removal. On May 15, 2018 7:04:31 AM PDT, Bert Gunter

Dear Rdevelopers The background for this email is that I was helping a PhD student to improve the speed of her R code. I suggested to replace calls like t(AA)%*% BB by crossprod(AA,BB) since I expected this to be faster. The surprising result to me was that this change actually made her code slower. > ## Examples : > > AA <- matrix(rnorm(3000*1000),3000,1000) > BB <-

Unless there is good reason not to, always cc the list -- there are lots of smarter folks than I on it who can help. I may or may not have time to look at this. Hopefully someone else will. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip

Hi, Couldn't is.unsorted() bail out immediately here (after comparing the first 2 elements): > x <- 20000000:1 > system.time(is.unsorted(x), gcFirst=TRUE) user system elapsed 0.084 0.040 0.124 > x <- 200000000:1 > system.time(is.unsorted(x), gcFirst=TRUE) user system elapsed 0.772 0.440 1.214 Thanks! H.

OK, Let's try this again! Here is the reproducible script; it is long because I had to copy the panel dataset here. My question is related to systemfit; I don't know how to get the result for the entire panel. #Reproducible script Empdata<- read.csv("/Users/ngwinuiazenui/Documents/UPLOADemp.csv") View(Empdata) install.packages("systemfit")

Sadly you failed to set your email program to send plain text and the data is corrupted at my end. I also think you need to reduce the size of the data set... the intent here is to increase your understanding, not debug your particular analysis. I will say that I am having a very challenging time understanding what you are trying to accomplish though. What are the equations that you think need

write.table with large data frames takes quite a long time > system.time({ + write.table(df, '/tmp/dftest.txt', row.names=FALSE) + }, gcFirst=TRUE) user system elapsed 97.302 1.532 98.837 A reason is because dimnames is always called, causing 'anonymous' row names to be created as character vectors. Avoiding this in src/library/utils, along the lines of Index:

Hi, people. A tiny suggestion for the system.time function. Could the returned vector have names? These could be like: c("User", "System", "Elapsed", "Sub.User", "Sub.System") That would then produce self-documenting output. -- Fran?ois Pinard http://pinard.progiciels-bpi.ca

Dear all, I usually run R on my laptop with Windows XP Professional. Now I really want to run R on a computer cluster (4 processors) with Suse Linux Enterprise ver. 10. But I am new with computer cluster. Should I modify my functions in order to use the greater performance and availability than that provided by my laptop? Is there any R manual on parallel computations on

Hola! This is 64bit Lubuntu 13.04, with R-3.0.0 installed from deb http://ppa.launchpad.net/marutter/rrutter/ubuntu raring main via synaptic. gcc --version gcc (Ubuntu/Linaro 4.7.3-1ubuntu1) 4.7.3 > sessionInfo() R version 3.0.0 (2013-04-03) Platform: x86_64-pc-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8

Has anyone successfully compiled pnmath (http://www.stat.uiowa.edu/~luke/R/experimental ) for an intel processor running mac OS 10.5? When I attempt to do so via the R package installer (choosing "Local Source Package" and pointing to the pnmath_0.0-2.tar.gz file), I get the following errors: * Installing *source* package 'pnmath' ... ** libs ** arch - i386 gcc -arch i386

Full_Name: Alessandra Iacobucci Version: 2.5.1 OS: Mac OS X 10.4.11 Submission from: (NULL) (193.48.71.92) Hi, I am making some intensive simulations for the testing of a Population Monte Carlo algorithm. This involves also a study of the CPU times in two different case. What I am trying to measure is the "real" CPU time, the one which is independent on the %CPU. I'm using the

(1) ...need to speed up a monte-carlo sampling...any suggestions about how I can get R to use all 8 cores of a mac pro would be most useful and very appreciated... (2) spent the last few hours trying to get pnmath to compile under os- x 10.5.4... using gcc version 4.2.1 (Apple Inc. build 5553) as downloaded from CRAN, xcode 3.0... ...xcode 3.1 installed over top of above after

Hello Everyone, I just started a new job & it requires heavy use of R to analyze datasets. I have a data.table that looks like this. It is sorted by ID & Date, there are about 150 different IDs & the dataset spans 3 million rows. The main columns of concern are ID, date, and totret. What I need to do is to derive daily returns for each ID from totret, which is simply totret at time

Hi, I'm trying to write a general-purpose "lexicon" class and associated methods for storing and accessing information about large numbers of specific words (e.g., their frequencies in different genres). Crucial to making such a class practically useful is to get hashing working correctly so that information about specific words can be accessed quickly. But I've never really

I have an "embarrassingly parallel" routine that I need to run 24000^2/2 times (based on some microarray data). All I really need to do is parallelize a nested for-loop. But I haven't found a clear list of what packages/commands I'd need to do this. I've got a dual quad core xeon system running RHEL5, so if I could use hyperthreading to increase the number of (virtual)

Hi, I have a 5x731 array A, and I want to compute the sums of the columns. Currently I do: apply(A, 2, sum) But it turns out, this is slow: 70% of my CPU time is spent here, even though there are many complicated steps in my computation. Is there a faster way? Thanks, Martin

Hi, I am experiencing a long delay when using dataframes inside loops and was wordering if this is a bug or not. Example code: > st <- rep(1,100000) > ed <- rep(2,100000) > for(i in 1:length(st)) st[i] <- ed[i] # works fine > df <- data.frame(start=st,end=ed) > for(i in 1:dim(df)[1]) df[i,1] <- df[i,2] #takes for ever R: R 2.0.0 (2004-10-04) OS: Linux, Fedora Core 2

hi all If I wanna get the total number of lines in a big file without reading the file's content into R as matrix or data frame, any methods or functions? thanks in advance. Regards

Hello, I have question about the overhead in lapply. x is a list of 3000 lists. Each of the i (1<=i<=3000) list elements is pair of two elements: a string vector and a data frame x is roughly 235MB. > gc() ## > z <- system.time(y <- lapply(x,function(r){ system.time(serialize(r,NULL))['elapsed'] })) > sum(unlist(y)) 18.812 > z user system elapsed 494.144