similar to: sum over extremely small numbers

Greetings. For R gurus this may be a no brainer, but I could not find pointers to efficient computation of this beast in past help files. Background - I wish to implement a Cramer-von Mises type test statistic which involves double sums of max(X_i,Y_j) where X and Y are vectors of differing length. I am currently using ifelse pointwise in a vector, but have a nagging suspicion that there is a

Hi all, I'm not familiar with R programming and I'm trying to reproduce a result from a paper. Basically, I have a dataset which I would like to model in terms of successive increments, i.e. (y denote empirical values of y) y_1 = y1, y_2 = y1 + delta1, y_3 = y1 + delta1 + delta2. ... y_m = y1 + sum_2^m delta j where delta_j donote successive increments in the y-values, i.e. delta

I want to maximize a conditional likelihood function that is basically logistic conditional on the number of successes within strata. What would be a good starting place for this? A complication is that the denominator includes a term that is the sum over all permutations. Although there is no time dimension to the problem, it's possible a degenerate use of the Cox proportional hazards

>> I have come across your reported log1p error (#2837) on a NetBSD (1.6W) >> system. I've just made further experiments on the deficient log1p() function on OpenBSD 3.2 and NetBSD 1.6 with this test program: % cat bug-log1p.c #include <stdio.h> #include <stdlib.h> #include <math.h> int main(int argc, char* argv[]) { int k; double x; for (k = 0; k

Yes, you're right. In fact, it's just an adaptation of a matlab command and the author advises using N^4 replications that's why it's the default in the function. The bistochastic matrix is not my subject of interest, but I need it to perform some random tranformation of a vector of incomes. Florent Bresson ----- Message d'origine ---- De : Richard M. Heiberger <rmh at

Full_Name: M Welinder Version: 1.4 OS: (src) Submission from: (NULL) (192.5.35.38) It seems to me that pexp can be improved in the lower_tail=TRUE and log_p=FALSE case by using expm1. Something like -expm1 (-x / scale); I think. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

The derivative table resides in the function D. In S+ that table is extensible because it is written in the S language. R is faster but less flexible, since that table is programmed in C. It would be useful if R provided a mechanism for extending the derivative table, or barring that, provided a broader table. Currently unsupported mathematical functions of one argument include expm1, log1p,

Dear List, I'm just teaching myself semi-parametric techniques. Apologies in advance for the long post. I've got observational data and a longitudinal, semi-parametric model that I want to fit in GAM (or potentially something equivalent), and I'm not sure how to do it. I'm posting this to ask whether it is possible to do what I want to do using "canned" commands

Full_Name: Morten Welinder Version: 1.5.1 OS: Solaris/Linux Submission from: (NULL) (192.5.35.38) The line return log(1 - p) * (x + 1); looks like it has problems for p near 1. I would suggest rewriting it to return log1p (-p) * (x + 1); -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read

Hello, I am trying to use Yan and Rossini's Makefile for cross building Windows versions of R packages in Linux with R 1.9.0. When compiling R with the mingw tools I get an error about expm1 being undeclared when first found at src/main/arithmetic.c:1019 If I fiddle a bit with it later on I also get errors about log1p bein undeclared. Any idea what should I look for? I am using R 1.9.0 in

Hello, I am trying to use Yan and Rossini's Makefile for cross building Windows versions of R packages in Linux with R 1.9.0. When compiling R with the mingw tools I get an error about expm1 being undeclared when first found at src/main/arithmetic.c:1019 If I fiddle a bit with it later on I also get errors about log1p bein undeclared. Any idea what should I look for? I am using R 1.9.0 in

Hi, With R version 2.10.0 (2009-10-26) on Windows, I computed the p=1.e-20 quantile of the geometric distribution with parameter prob=0.1. > qgeom(1.e-20,0.1) [1] -1 But this is not possible, since X=0,1,2,... I guess that this might be a bug in the quantile function, which should use the log1p function, instead of the naive formula. Am I correct ? Best regards, Micha?l

I've looked around and I just can't find anything that will work for my needs. This is a bit of a 2 part question but pertaining to the same topic so bare with me. The first is with my qq plot. On the Y axis of my qq plot it'll have my sample quantities but because my data is log-normal it'll show numbers between 0 - 5 (depending on the data). I'd like to know how to get it,

Dear People, I am trying to use R's Math library as standalone, as documented in /src/nmath/standalone. I am using C++ in Debian testing, and the versions are as follows: ii g++-3.0 3.0.3-1 The GNU C++ compiler. ii r-mathlib 1.4.0-1 `GNU S' - Standalone R math library I have a file (rand.cc) as follows. I don't think that lattice.hh or mh.hh are very

There is a small typo in the Source section of the help page for log1p: Source: 'log1p' and 'expm1' may be taken from the operating system, but if not available there are based on the Fortran subroutine 'dlnrel' there -> they Jen > sessionInfo() R version 3.0.2 (2013-09-25) Platform: x86_64-unknown-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_US.UTF-8

Thanks, I tried someting like this, but computation takes times for large matrices btransf <- function(y,X=length(y)^4) { N <- length(y) bm <- matrix(rep(1/N,N^2),N,N) for(j in 1:X){ coord <- sample(1:N,4,replace=T) d <- runif(1,0,min(bm[coord[1],coord[2]],bm[coord[3],coord[4]]))

I have a 30 x 27 data frame, which I'm trying to scale and transform. Only I want to scale certain all variables except one (the dependent, which I want to log+1 transform). I can use split() and then scale() and log1p(), but I'm wondering if I can do this in one call. I tried apply(), but I could only get the whole data frame, not a part of it: > test <- apply(chin.sub, 2, log1p)

Hi, everyone I am trying to estimate 3 parameters for my survival function. It's very complicated. The negative loglikelihood function is: l<- function(m1,m2,b) -sum( d*( log(m1) + log(m2) + log(1- exp(-(b + m2)*t)) ) + (m1/b - d)*log(m2 + b*exp(-(b + m2)*t) ) + m1*t - m1/b*log(b+m2) ) here d and t are given, "sum" means sum over these two vairables. the parameters

Do you heve any idea why I get after this instruction everywhere false? > seq (0, 1, by=0.1) == 0.3 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE But after different step it's ok: > seq(0, 1, by=0.1) == 0.4 [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE -- View this message in context:

I have hit a problem with the design of the mcmc package I can't figure out, possibly because I don't really understand the R function call mechanism. The function metrop in the mcmc package has a ... argument that it passes to one or two user-supplied functions, which are other arguments to metrop. When the two functions don't have the same arguments, this doesn't work.