similar to: Load the shared object/DLL

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] *

Hi, I compiled a C program file on Borland C++ 5.5 compiler to get one dll output (as instructed in the file readme.package). The C program file is just the example on page 31 of "writing R extensions": void convolve(double *a, int *na, double *b, int *nb, double *ab) { int 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

Hi all, i have some problem in using shared objects. I tried the example found in "Writing R Extension", pg. 33 I used the .c file prova.c : ------------------------------------------------------------------- #include <R.h> #include <Rinternals.h> void convolve(double *a, int *na, double *b, int *nb, double *ab) { int i, j , nab = *na + *nb - 1; for (i = 0; i < nab;

Hi, I am hoping some one can help me. I am learning to use C and would like to learn how to call c code in R. I have look at Writing R Extensions and I tried to copy the example on page 38 void convolve(double *a, int *na, double *b, int *nb, double *ab) { int 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 <

Hi everyone! I'm trying to lear how to call external C code in R but even the R help web-page example is drive me crazy. I copy-paste the example at: http://stat.ethz.ch/R-manual/R-patched/doc/manual/R-exts.html#Interface%20functions%20.C%20and%20.Fortran here is the example: void convolve(double *a, int *na, double *b, int *nb, double *ab) { int i, j, nab = *na + *nb - 1; for(i = 0;

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

Dear all, I'm studing how to include .c code in my .r functions . In the R-exts.pdf manual I have found the following code. At one point the author write "called from R by"... How can I load a .c file on R? ( I am using a xp windows as so.) Thank you ale void convolve(double *a, int *na, double *b, int *nb, double *ab) { int i, j, nab = *na + *nb - 1; for(i = 0; i < nab; i++)

Hi, I am happy to announce that I finished the translation of almost all LGPL'ed R headerfiles to Delphi. As a test I did several demos which basically contain delphized versions of all the examples of chapter 5 in "Writing R Extensions". Please have a look in the attachments for details (DemosAndHeaders.txt), more information (Readme.txt) and an example of how it looks

Hi, every one, I have translated <An Introduction to R > into simplified Chinese and traditional Chinese. You can browse them from these two URL: simplified Chinese: http://www.biosino.org/pages/newhtm/r/schtml/index.html#Top traditional Chinese: http://www.biosino.org/pages/newhtm/r/tchtml/ I have distributed these document in HTML format. That is because there is something wrong with

Hi folks, My system is Windows98 + R1.9.0. The path for my system is c:\perl\bin; c:\mingw\bin; c:\rtools; c:\windows; c:\windows\command; c:\rw1090\bin. I created three files followed the examples in “Writing R extensions” in the directory c:\temp: // X.hh class X { public: X (); ~X (); }; class Y { public: Y (); ~Y (); }; // X.cc #include <iostream> #include "X.hh" static

Hi, Man page for 'convolve' says: conj: logical; if 'TRUE', take the complex _conjugate_ before back-transforming (default, and used for usual convolution). The complex conjugate of 'x', of 'y', of both? In fact it seems that it takes the complex conjugate of 'y' only which is OK but might be worth mentioning because (1) conj=TRUE is the

We obtained some disturbing results from convolve() (inaccuracies and negative probabilities). We'll try to make the context clear in as few lines as possible... Our function panjer() (code below) basically computes recursively the probability mass function of a compound Poisson distribution. When the Poisson parameter lambda is very large, the starting value of the recursive scheme ---

Dear R users, I want to call C code via the .C or .Call interface. This works fine with integer values but using doubles the array received in C will be set to zeros. I have tried the convolve examples (Writing R extensions, chapter 5.2) and still the resulting array consists of zeros. My code (shortened for my purposes. Original did not work either):

Hi, I'm new to R, and I'm a bit confused with the "convolve()" function. If I do: x<-c(1, 2, 3) convolve(x, rev(x), TRUE, "open") = 9 12 10 4 1 But I expected: 3 8 14 8 3 (like in Octave/MATLAB - conv(x, reverse(x)) ) 3 2 1 x 1 2 3 = 3 2 1 0 6 4 2 0 0 9 6 3 = 3 8 14 8 3 The thing is, that "convolve(x, x, TRUE, "open")" works. For me

Dear friends, The user coordiante system in the R graphics is easy to understand as it simply corresponds to the range of values on the axes of the plot. However, sometimes, we want to kown the coordinates of a region in other system, e.g., the normal coordinate system whose origin is the the upper-left cornner of the drawing area which is used to set the hot regions in the HTML image maps.

Hi, the function stats::convolve does not mention efficient usage of the underlying FFT algorithm, such as (a) if type="circular", then length(x)=length(y) should have many factors (e.g. length(x) = length(y) = 2^n) (b) if type="open" or "filter", then length(x)+length(y)-1 should have many factors (e.g. length(x)+length(y)-1 = 2^n) In particular the latter may

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

Dear R-ophiles, I've found something very odd when I apply convolve to ever larger vectors. Here is an example below with vectors ranging from 2^11 to 2^17. There is a funny bump up at 2^12. Then it gets very slow at 2^16. > for( i in 11:20 )print( system.time(convolve(1:2^i,1:2^i,type="o"))) user system elapsed 0.002 0.000 0.002 user system elapsed 0.373

Dear all, I have an image that I need to filter, and so I'm looking for a method to convolve it with a matrix. If I understood the docs for convolve correctly, it only works in 1D (and I have tried to convolve, it didn't look good). So, I wondered if anybody have implemented 2D convolution in R, or have any good advices to share (beyond having a look at mvfft), before I go hacking?

Dear All, I am not familar with R. I want to use PCA (principal components analysis) and MDS (multidimensional scaling). Can someone tell me which R package I should use for PCA and MDS? I appreciate your help in advance. Ray