similar to: Just-in-time compiler for R

The topic of Ra and jit has come up on this list recently (see http://www.milbo.users.sonic.net/ra/index.html) so I thought people might be interested in this little demo. For it I used my machine, a 3-year old laptop with 2Gb memory running Windows XP, and the good old convolution example, the same one as used on the web page, (though the code on the web page has a slight glitch in it). This

? data=read.table("http://statcourse.com/research/boston.csv", , sep=",", header = TRUE) ? library(rpart) ? fit=rpart (MV~ CRIM+ZN+INDUS+CHAS+NOX+RM+AGE+DIS+RAD+TAX+ PT+B+LSTAT) Please: Show me the tree. Mark -------- Original Message -------- Subject: Re: [R] help with rpart From: "Stephen Milborrow" <[1]milbo at sonic.net>

Viewing certain postscript files under R 2.13-0 is very slow, at least using GSview on my 64 bit Windows 7 system. To see this, compare how long it takes to display these two files (fast.ps was created by removing the /srgb stuff in .ps.prolog): www.milbo.org/postscript/slow.ps www.milbo.org/postscript/fast.ps The code to produce the above two files is here:

On 25/10/2008 10:45 AM, milbo at sonic.net wrote: > Full_Name: Stephen Milborrow > Version: R 2.8.0 > OS: Windows XP > Submission from: (NULL) (81.159.65.46) > > > Under certain conditions, R 2.8.0 incorrectly echoes integer literals without > the "L" suffix. R 2.7.2 is ok. > > To reproduce, create two files as follows: > a.R containing:

Full_Name: Stephen Milborrow Version: R 2.8.0 OS: Windows XP Submission from: (NULL) (81.159.65.46) Under certain conditions, R 2.8.0 incorrectly echoes integer literals without the "L" suffix. R 2.7.2 is ok. To reproduce, create two files as follows: a.R containing: source("b.R", echo=TRUE) b.R containing: x <- 1L Now execute: Rterm --vanilla --silent <a.R This

In R, 0 ^ NaN yields Inf. I would have expected NaN or perhaps 0. Is this behaviour intended? >sessionInfo() R version 2.8.0 (2008-10-20) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 attached base packages: [1] stats graphics grDevices utils

Greetings R developers, R will run a little faster when executing "pure R" code if the function R_CheckStack() is modified. With the modification, the following code for example runs 15% faster (compared to a virgin R-2.5.1 on my Windows XP machine): N = 1e7 foo <- function(x) { for (i in 1:N) x <- x + 1 x }

Stephen, thanks for your effort. The more appropriate list for this discussion is probably R-devel (as far as I understand it) so I've moved the discussion there. Related topics have already been discussed in the past. Specifically, I remember contributions by Paul Johnson ("rockchalk" package) and John Fox ("effects" and "car" package) as their packages

To add to the wish-list for "convolve": For modeling processes that decay exponentially in time, e.g., fluorescence, it is desirable to have a function that convolves an arbitrary vector with an exponential using an iterative method. In the TIMP package (which won't be on CRAN till R 2.5.0 is official, but is for now at www.nat.vu.nl/~kate/TIMP) we implemented this special-purpose

Dear R People: Here is something that I am sure is very simple. I'm just trying to re-create the C convolution example in the Extensions manual. Here is the subroutine: void convolve(double *a, int *na, double *b, int *nb, double *ab) { R_len_t i, j, nab = *na + *nb - 1; for(i = 0; i < nab; i++) ab[i] = 0.0; for(i = 0; i < *na; i++) for(j = 0; j < *nb; j++) ab[i + j] += a[i] *

I've continued to work on speeding up R, and now have a collection of fourteen patches, some of which speed up particular functions, and some of which reduce general interpretive overhead. The total speed improvement from these patches is substantial. It varies a lot from one R program to the next, of course, and probably from one machine to the next, but speedups of 25% can be expected in

Jeff Brown wrote: > > Hi, > > I'm trying to learn to use .C, which lets one invoke compiled C code from > within R. To do that, one has to first get the C code into R as a shared > object, which (I think) means first compiling it (with COMPILE or SHLIB) > and then loading it (with dyn.load()). > I would suggest taking it a step further and building an R package

> Looking for a simple, effective a minimum execution time solution. > > For a vector as: > > c(0,0,1,0,1,1,1,0,0,1,1,0,1,0,1,1,1,1,1,1) > To transform it to the following vector without using any loops: > (0,0,1,0,1,2,3,0,0,1,2,0,1,0,1,2,3,4,5,6) > Appreciate any suggetions. [[alternative HTML version deleted]]

I'm curious if anyone has any plugins for AV integration directly into dovecot. Our old pop servers have been scanning messges as they're moved from new->cur in the inbox and, at least where user's aren't poping every few seconds, there is occasionally enough time between scanning through the MXs to message retreval to snag a few more virues with updated definitions before

Hello all useRs, I am tring make a simple surface plot ( 2 by 2 terms of a MARS model (with earth package) but I get the follow error message: > plotmo( mars ) Error: bad index (missing column in x?) I don't no how to workround this... :-( I thanks in advanced by some help! Thanks. Cleber ############### > > ### example code: > library( earth ) > data( gasoline,

It appears to be legitimate to include multi-level categorical and continuous variables in defining the model for earth (e.g. y ~ cat + cont1 + cont2) but is it also then possible use categoricals in the predict method using the earth result? I tried but it returns an error which is not very informative. Thanks Chris

I've continued to work on speeding up R, and now have a collection of fourteen patches, some of which speed up particular functions, and some of which reduce general interpretive overhead. The total speed improvement from these patches is substantial. It varies a lot from one R program to the next, of course, and probably from one machine to the next, but speedups of 25% can be expected in

Dear R-Helpers, I want to try the 2D (two-dimensional) convolution in R. For example, let us we have the following kernel and data. kernel <- (1,2,3,2,1) data <- array(1:100, dim=c(10,10)) I know the function 'convolve' only for one-dimensional convolution, but it is just for a 1D sequence. Is there any function for 2D convolution? For theory, please refer to the following link:

I have been experimenting with convolve(). What I know about convolution I learned from engineering (they call this stuff linear systems theory), not statistics, so maybe this is all just a matter of different conventions. BUT I notice very weird things with convolve(). 1. First example, from the classic Bracewell The Fourier transform and its applications, chap 3 (p.32 in 2nd edition): {2 2 3 3

Full_Name: Bill Simpson Version: 65.1 , 0.90.1 OS: Linux Submission from: (NULL) (193.62.250.209) I reported this on r-help, but here is official bug report. The present convolve() does not do convolution by default. Its default behaviour is correlation. This is a bug. The default argument conj should be set to FALSE. The zero-padding should be on the right for linear convolution (don't