similar to: Compiling R code to native code?

Dear all, I have an R script that uses Rcpp, and I have been trying to parallelize it using mclapply (I tried with the multicore and the parallel library) Sometimes (not always, interestingly), the CPU use for each core drops, usually so that the total over all cores reaches 100%, i.e., as fast as if using just one single core fully. I tried my code directly from within emacs, and also using a

Jeff, I wanted to let you know that your function is faster than generating the directional circular permutations and weeding. Here is the time for n = 10. I compared with just doing the permutations, there is no point in proceeding further with the weeding since it is slower at the start itself. system.time(directionless_circular_permutations(10)) user system elapsed 1.576 0.000

Hi, I built a package in linux and would like to use it in windows. I cant build the package again in windows because it would miss the necessary libraries etc(I'm using Rcpp). So I need to generate a binary version and try to install it in windows. I tried this and I get the error /cygdrive/RTools/bin/cp: target 'xf->>' is not a directory Can someone help me with this? --

Thanks! Yes, however, this seems a bit wasteful. Just wondering if there are other, more efficient options possible. Best wishes, Ranjan On Thu, 29 Mar 2018 22:20:19 -0400 Boris Steipe <boris.steipe at utoronto.ca> wrote: > If one is equal to the reverse of another, keep only one of the pair. > > B. > > > > > On Mar 29, 2018, at 9:48 PM, Ranjan Maitra

New function below is a bit faster due to more efficent memory handling. for-loop FTW! directionless_circular_permutations2 <- function( n ) { n1 <- n - 1L v <- seq.int( n1 ) ix <- combinations( n1, 2L ) jx <- permutations( n-3L, n-3L ) jxrows <- nrow( jx ) jxoffsets <- seq.int( jxrows ) result <- matrix( n, nrow = factorial( n1 )/2L, ncol = n ) k

I don't know if this is more efficient than enumerating with distinct directions and weeding... it seems kind of heavyweight to me: ####### library(gtools) directionless_circular_permutations <- function( n ) { v <- seq.int( n-1 ) ix <- combinations( n-1, 2 ) jx <- permutations( n-3, n-3 ) x <- lapply( seq.int( nrow( ix ) ) , function( i ) {

Are they the same with .RData being the newer format?? Thanks, ...Tao

Hi, I'm trying to user lmer function from lmerTest package because, if I understood correectly, it allows to make better inference than lmer method from lme4 package. However, whatever I do I keep getting this error: Error in lme4::lFormula(formula = mark ~ ssCount + sTime+ : rank of X = 1660 < ncol(X) = 1895 any ideas what could be a problem? thanks, Srecko [[alternative HTML

Thanks, Here's the error, for example: > install.packages("mgcv") Installing package into 'C:/Users/pl39/Documents/R/win-library/3.4' (as 'lib' is unspecified) trying URL 'https://mirrors.nics.utk.edu/cran/bin/windows/contrib/3.4/mgcv_1.8-23.zip' Content type 'application/zip' length 2775362 bytes (2.6 MB) downloaded 2.6 MB package 'mgcv'

Hello r-help, I've been banging my head against the computer in an attempt to learn how to divide my matrix into segments by rows. I want to be able to return each segment as a newly named object. I've tried looking at the apply functions and creating a for loop but brain no work. Here's the basic starting objects that I believe would be needed to separate the matrix. mat <-

Dear R users, I am running simulations (1000), and in my simulation I am looking at specific sums. For example, if the sum is >=4 then count this, if say <3, then don't count, if the sum=3, then generate a random number from uniform distribution, if this number is say less than 0.5, then count this sum, if greater than 0.5, then don't count. I am having trouble with introducing this

Hi all: My OS is 32bit winxp,but I wanna install 64bit R2.14.1. >From the following website,it says "You can also go back and add 64-bit components to a 32-bit install, or vice versa" http://cran.r-project.org/bin/windows/rw-FAQ.html#Can-both-32_002d-and-64_002dbit-R-be-installed-on-the-same-machine_003f Does it mean that I can install and run 64bit R2.14.1 under 32bit

In the following data.frame there are 6 columns, but 7 are written to the CSV file. install.packages("pmlr") library(pmlr) data(enzymes) write.table(enzymes, sep=",", eol="\n",file="albert.csv")

Hi, I am trying to normalize some data. First I fitted a principal curve (using the LCPM package), but now I would like to apply a transformation so that the curve becomes a "straight diagonal line" on the plot. The data used to fit the curve would then be normalized by applying the same transformation to it. A simple solution could be to apply translations only (e.g., as done after a

Hello R, could you tell me please, how to fix this issue? There is installed RStudio-2.15.2 and the next steps where follow but the message appears: http://cran.r-project.org/web/packages/RMySQL/INSTALL): > > 1. Download the RMySQL_0.9.tar.gz file to the folder C:\Program > Files\R\R-2.15.1\bin\x64 > 2. Open a Windows command line, and change directory to that folder > 3.

Hello Folks, I'm trying to vectorize a loop that processes rows of a dataframe. It involves lots of conditionals, such as "If column 10 == 3, and if column 3 is True, and both column 5 and 6 are False, then set column 4 to True". So, for example, any ideas about vectorizing the following? df = data.frame( list(a=c(1,2,3,4),

Hello, i am working in origin pro, i want to plot a graph as like a pdf attached but with black and white lines. here radial axis varies from 0 to 1. and angular axis from 0 degree to 60 degree.and third axis which is depend on both radial and axial gives non intersecting lines. how can i read the data from plot for replot. vikas -------------- next part -------------- A non-text attachment was

I have two regressions to perform - one with a metric DV (-3 to 3), the other with an ordered DV (0,1,2,3). Neither normal distribution not homoscedasticity is given. I have a two questions: (1) Some sources say robust regression take care of both lack of normal distribution and heteroscedasticity, while others say only of normal distribution. What is true? (2) Are there ways of using robust

is it possible to throw a stop() that is so hard that it will escape even tryCatch? /iaw ---- Ivo Welch (ivo.welch at gmail.com)

Dear all, In a plot command like xyplot(Y~X|ID*PERIOD,data=...) xyplot will generate all the possible ID*PERIOD combinations. But not all of them have data in there. So I have a lot of empty plots. How can I suppress those empty plots and ask xyplot only to generate plots actually with data. Thanks. Jun [[alternative HTML version deleted]]