similar to: help, what's CarbonLib 1.20?

The following is at least as much out of intellectual curiosity as for practical reasons. On reviewing some code written by novices to R, I came across: crossprod(x, y)[1,1] I thought, "That isn't a very S way of saying that, I wonder what the penalty is for using 'crossprod'." To my surprise the penalty was substantially negative. Handily the client had S-PLUS as

Dear all, Is it possible to run R 1.6.2 on Mac OS N1-8.1 after installing CarbonLib and InterfaceLib ? I downloaded CarbonLib Update 1.0.4 but it didn't work. Is this system too old ? I am trying to find a solution but if it is not possible it's a waste of time. Or should I use an older version of R ? Dirk [[alternate HTML version deleted]]

Dear all, Has anyone written R code for partial Mantel Tests- as described for instance in Legtendre & Legendre (1998) ? In other words, in a community ecology analysis, I would like to calculate the correlation between two dissimilarity matrices, controlling for a third distance matrix representing geographical distances between sites. Thanks! Christophe Bouget Biodiversité et gestion des

ABCD are four matrix. A * Inverse((Transpose(A)*Tranpose(B)*B*A+C)) * Transpose(A) * Transpose(B) * D how to write in R in an efficient way? --------------------------------- [[alternative HTML version deleted]]

hi, the help page for crossprod states that crossprod(A,B) is faster than t(A) %*% B; experimentation certainly bears this out. more alarming is the evidence that crossprod(t(A), B) is faster than A %*% B: on a PII laptop, 128MB memory, win98, R-1.5.0.-patched precompiled (no ATLAS): > A <- matrix(rnorm(250000),500,500) > B <- matrix(rnorm(250000),500,500) > for (i in 1:5) {

> From: Douglas Bates [mailto:bates at bates4.stat.wisc.edu] > > "Liaw, Andy" <andy_liaw at merck.com> writes: > > > Sorry. I need to retract my claim. There seems to be a 3G > limit, even > > though the OS could handle nearly 8G. (I can have two > simultaneous R > > processes each using near 3G.) > > > > On another note, on

> From: Douglas Bates [mailto:bates at bates4.stat.wisc.edu] > > "Liaw, Andy" <andy_liaw at merck.com> writes: > > > Sorry. I need to retract my claim. There seems to be a 3G > limit, even > > though the OS could handle nearly 8G. (I can have two > simultaneous R > > processes each using near 3G.) > > > > On another note, on

I tried to intall R in a iMac with Mac OS E1- 9.0.4 (Mac OS ROM VE-4.6.1). When I tried to run R for the first time I got a message about "Carbonlib-- CF RangeMake" not beeing found. Please, could you help me to tackle the problem. Thanks very much. Barahona -- Enrique Barahona Fernandez ESTACION EXPERIMENTAL DEL ZAIDIN, CSIC Profesor Albareda, 1 18008, GRANADA (Spain) Tel. + 34

The solve function in r-patched has been changed so that it applies a tolerance when using Lapack routines to calculate the inverse of a matrix or to solve a system of linear equations. A tolerance has always been used with the Linpack routines but not with the Lapack routines in versions 1.7.x and 1.8.0. (You can use the optional argument tol = 0 to override this check for computational

Dear List, I am aware this is slightly off-topic, but I am sure there are people who already had the problem and who perhaps solved it. I am running long-lasting model fits using constrOptim command. At work there is a linux computer (Quad Core, debian) on which I already have compiled R and Atlas, in the hope that things will go faster on that machine. Atlas offers the possibility to be

Hi all, I managed to at least compile R-patched (2002-01-08) against MKL 5.1 (not beta). The release notes for MKL said that the libraries are threaded, and the test codes needed to be linked against the pthread library. Therefore I added -lpthread flag in config.site for R-patched, and the compile went through. However, when I did make check, it choked on a call to La.eigen, complaining that

Hi, I'm relatively new to R on Ubuntu (moving from Windows), and I'm trying to get GotoBLAS2 working. I installed (from a CRAN mirror) the pre-built binaries of R (which, as far as I know, is compiled as a shared library) on Ubuntu 10.10 using apt-get install r-base r-base-dev I successfully built GotoBLAS2 from source, copied the library to /usr/lib and created s symbolic link from

As far as I can see by searching the archieves, R could not take advantage of two processors in the same machine but there were plans for implementing multithreading in R. Is this work going on? On the other hand, if two processors are present in the same linux machine is it possible to focus one processor on tasks of the operative system and the other on R and/or other real computing? Could

Hello, I already download rm160.sit to a iMac9.1 computer. But I can not run R-icon for installation of R. The computer told me --the application "R" could not be opend because "Carbonlib" could not be found. I donot know why I could not install R to the computer. Thank you. Jinbo __________________________________________________ Faith Hill - Exclusive Performances,

Hi all, I've compiled R (version 2.8.1) from the source code with "--with- blas=" option in order to use external multi-threaded blas package. However, it's very hard to verify that compilation is correct and R is using multi-threads correctly or not. Which command or operation in R will run with multi-threads? I assumed that matrix multiplication %*% may call multi-threaded

Hi, For those who are interested, I have update my R benchmark (to version 1.5.0) and also to Splus 6. They are available at: http://www.sciviews.org/other/benchmark.htm. A comparison is made between Matlab (5.3 & 6), R 1.5.0, Splus 6 rel 2, O-Matrix 5.1, Octave 2.1.31, Scilab 2.6, Rlab 2.1 and OX 3.00 under Windows 2000 pro. Overall, R is not the fastest package, but it is one of the

Hi I have a square matrix Ainv of size N-by-N where N ~ 1000 I have a rectangular matrix H of size N by n where n ~ 4. I have a vector d of length N. I need X = solve(t(H) %*% Ainv %*% H) %*% t(H) %*% Ainv %*% d and H %*% X. It is possible to rewrite X in the recommended crossprod way: X <- solve(quad.form(Ainv, H), crossprod(crossprod(Ainv, H), d)) where quad.form() is a little

Hi, I'd like to install the optimized libraries on my Thinkpad with an Intel Core Solo chip, which apparently supports the following instruction sets: MMX, SSE, SSE2, SSE3 instruction sets, XD-Bit. In a recent r-help message, the following was said (by Dirk I think): <quote> Yes, on Ubuntu, use 'apt-get install atlas3-base' for basic tuned Atlas, or 'apt-get install

Hi the manpage says that crossprod(x,y) is formally equivalent to, but faster than, the call 't(x) %*% y'. I have a vector 'a' and a matrix 'A', and need to evaluate 't(a) %*% A %*% a' many many times, and performance is becoming crucial. With f1 <- function(a,X){ ignore <- t(a) %*% X %*% a } f2 <- function(a,X){ ignore <-

I found out the other day that crossprod() will take a single matrix argument; crossprod(x) notionally returns crossprod(x,x). The two forms do not return identical matrices: x <- matrix(rnorm(3000000),ncol=3) M1 <- crossprod(x) M2 <- crossprod(x,x) R> max(abs(M1-M2)) [1] 1.932494e-08 But what really surprised me is that crossprod(x) is slower than crossprod(x,x): R>