similar to: strange timings in convolve(x,y,type="open")

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

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

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

Hi I have made a R function 'convolve2' for convolution of two real valued vectors based on Rs 'convolve' with option type="open" - see below. (exp.length and irf.length are variables set in another part of the program) I wish to implement the function convolve2 in C and use it in a function used from R with .Call - e.g. I need to call fft in C. All I can find in the

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

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 ---

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

Dear R-users, I am trying to translate a matlab code for calculating the Local Whittle estimator in time series with long memory originally written by Shimotsu and available free in his webpage ( http://www.econ.queensu.ca/pub/faculty/shimotsu/ ) The Matlab code is ======================================================================================= function[r] = whittle(d,x,m) % WHITTLE.M

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):

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?

Hi all I have run into a case where I don't understand why predict.lrm and predict.glm don't yield the same results. My data look like this: set.seed(1) library(Design); ilogit <- function(x) { 1/(1+exp(-x)) } ORDER <- factor(sample(c("mc-sc", "sc-mc"), 403, TRUE)) CONJ <- factor(sample(c("als", "bevor", "nachdem",

Hello everybody When one is working with complex matrices, "transpose" very nearly always means *Hermitian* transpose, that is, A[i,j] <- Conj(A[j,i]). One often writes A^* for the Hermitian transpose. I have only once seen a "real-life" case where transposition does not occur simultaneously with complex conjugation. And I'm not 100% sure that that wasn't a

I'm doing convolutions in the frequency domain and need to know the layout of the Fourier modes returned by fft. (This is leading up to a more involved question about moment generating functions, but I need to know if I've got this part correct first.) I think in 1D the pattern is: 0 1 2 3 -2 1 (even) 0 1 2 3 -3 2 1 (odd) In 2D is it simply (for a square matrix): 0 1 2 -1 (horizontal)

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, All: Is there a simple way to detect complex conjugates in the roots returned by 'polyroot'? The obvious comparison of each root with the complex conjugate of the next sometimes produces roundoff error, and I don't know how to bound its magnitude: (tst <- polyroot(c(1, -.6, .4))) tst[-1]-Conj(tst[-2]) [1] 3.108624e-15+2.22045e-16i

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

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

On two laptops running 32-bit kubuntu, I have found that svd(), invoked within R 2.9.1 as supplied with the current ubuntu package, returns very incorrect results when presented with complex-valued input. One of the laptops is a Dell D620, the other a MacBook Pro. I've also verified the problem on a 32-bit desktop. On these same systems, R compiled from source provides apparently

Hello everyone, I'm a new comer to this mailing list. I found that the echo_diagnostic.m included in speex-1.2rc1 seems to have grammar mistakes which cause parse error in octave 3.6.2. I wonder if anyone have ever executed this code under octave/matlab. I also made some minor modifications to the file to correct those errors and publish it here. If it can helps the project to be more

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++)