similar to: integrate over an infinite region produces wrong results depending on scaling

Not sure, but it seems the page contains a number of out-of-date entries: Pypy => pypy.org (link stale) plus: there is no llvm backend for pypy at the moment (although LLVM backends have been attempted a number of times, all seem to have failed) Unladen Swallow => not being developed since 2011 (http://qinsb.blogspot.com/2011/03/unladen-swallow-retrospective.html) TIA, Andreas The

Thanks for the report. The attached patch removes them from the page. OK? On 8 May 2013 11:34, Kostyrka (External user) Andreas <Andreas.Kostyrka at kapsch.net> wrote: > Not sure, but it seems the page contains a number of out-of-date entries: > > > > Pypy => pypy.org (link stale) plus: there is no llvm backend for pypy at the > moment (although LLVM backends have been

The following piece of IR is a fixed point for opt -std-compile-opts/-O3: --- target datalayout = "e-p:64:64:64-S128-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f16:16:16-f32:32:32-f64:64:64-f128:128:128-v64:64:64-v128:128:128-a0:0:64-s0:64:64-f80:128:128-n8:16:32:64" target triple = "x86_64-unknown-linux-gnu" ; Function Attrs: nounwind readonly define i32 @get32Bits(i8*

Dear all, Sorry if I am posting to the wrong place, but I could not find the link for registration on the bug tracker, that?s why I am writing here. I think there is inconsistency between two R functions from the stats package, bw.nrd0 and bw.nrd. Consider the following vector: D <- c(0, 1, 1, 1, 1) bw.nrd(D) returns zero bandwidth for this object even without a warning. Considering the

Hello, I have a question on unit root test with urca toolbox. First, to run a unit root test with lags selected by BIC, I type: > CPILD4UR<-ur.df(x1$CPILD4[5:nr1], type ="drift", lags=12, selectlags ="BIC") > summary(CPILD4UR) The results indicate that the optimal lags selected by BIC is 4. Then I run the same unit root test with drift and 4 lags:

Dear all, I have been implementing some bootstrap-related methods, and came across this theoretically undesirable behaviour in the computation of bootstrap quantiles. The manual says: ?Interpolation on the normal quantile scale is used when a non-integer order statistic is required.? Theoretically, when R=999 and (R+1)*alpha is integer, then, the calculations of the 95% CI should never contain

On Oct 27, 2013 2:16 PM, "David Nadlinger" <code at klickverbot.at> wrote: > > The following piece of IR is a fixed point for opt -std-compile-opts/-O3: > > --- > target datalayout = > "e-p:64:64:64-S128-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f16:16:16-f32:32:32-f64:64:64-f128:128:128-v64:64:64-v128:128:128-a0:0:64-s0:64:64-f80:128:128-n8:16:32:64"

Hi, I have a density that I need to get MLEs from, which includes definite integrals both in the denominator and in the numerator of the density function. It looks like the outcome depends on the initial values given. My program is shown below: library(circular) ######################################## 4 parameters ######################################## z<-rvonmises(100,0,1)

Dear all, I have some issues with integrate in R thus I would like to request your help. I am trying to calculate the integral of f(x)*g(x). The f(x) is a step function while g(x) is a polynomial. If f(x) (step function) changes its value only few times (5 or 6 'steps') everything is calulated ok(verified results in scrap paper) but if f(x) takes like 800 different values I receive the

I have been told by the CRAN administrators that the following code generated an error on 64-bit Fedora Linux (gcc, clang) and on Solaris machines (sparc, x86), but runs well on all other systems): > fn <- function(x, y) ifelse(x^2 + y^2 <= 1, 1 - x^2 - y^2, 0) > tol <- 1.5e-8 > fy <- function(x) integrate(function(y) fn(x, y), 0, 1,

Why I am getting Error in integrate(f, x1, x1 + dx) : maximum number of subdivisions reached and can I avoid this? func <- function(y, a, rate, sad){ f3 <- function(z){ f1 <- function(y,a,n){ dpois(y,a*n) } f2 <- function(n,rate){ dexp(n,rate) } f <- function(n){ f1(y,a,n)*f2(n,rate) } r <- 0 r1 <- 1 x1 <- 0 dx <- 20 while(r1 >

> version _ platform i686-pc-linux-gnu arch i686 os linux-gnu system i686, linux-gnu status major 1 minor 7.1 year 2003 month 06 day 16 language R Bug: integrate(f,lower,upper,extra_args) where f <- function(x,extra_args) { body } integrate doesn't pass the extra arguments when calling f. As a first check of this finding I integrated dnorm from

Hi, I'm trying to compile two functions into one function. the first funtion is called 'fs' which is self-made function, another function is from the built-in 'integration' function that is copy-paste-edited. If built separatey, these functions work well. However that is not the case if combines together, where certainly I made mistake somewhere when constructing the code.

Hi i am having a problem with the 'integrate' function the function i want to integrate has the form sum(vector^x) i have defined the function with a for loop first - integrandtotest <- function(x) {a<-rep(0,len=2) for (i in 1:2) {a[i]<-t[i]^x} sum(a) } the results gives errors ########### Error in integrate(integrandtotest, lower = 0.1, upper =

Hello everybody. I would like to pose a question regarding a warning message after running a function of my own. The function I created for computing the p-value of the dw statistic has a command line like this: »dwf0 <- function(dw,eigen) { carfun <- function(x) { (prod(1+2*(eigen-dw)*1i*x)^(-1/2)-prod(1-2*(eigen-dw)*1i*x)^(-1/2))/(1i*x) } ; 1/2+integrate(f=carfun,lower = 0,upper =

Hello, Rlisters I have to compute p-values that are on the tail of the distribution, P-values < 10^-20. However, my current implementations enable one to estimate P-values up to 10^-12, or so. A typical example is found below, where t is my critical value. ########### example - code adapted from Rassoc ####################### rho01 = 0.5 rho105 = 0.5 rho005 = 0.5 t = 8 z = 2

Hi, I noticed a minor flaw in integrate() from package stats: Taking up arguments lower and upper from integrate(), if (lower == Inf) && (upper == Inf) or if (lower == -Inf) && (upper == -Inf) integrate() calculates the value for (lower==-Inf) && (upper==Inf). Rather, it should return 0. Quick fix: ### old code ### ### [snip] else {

Dear @ll. I have to calculate numerical integrals for triangular and trapezoidal figures. I know you can calculate the exactly, but I want to do it this way to learn how to proceed with more complicated shapes. The code I'm using is the following: integrand<-function(x) { print(x) if(x<fx[1]) return(0) if(x>=fx[1] && x<fx[2]) return((x-fx[1])/(fx[2]-fx[1]))

Dear members, I'm getting an error with the "integrate" function. Searching in the r-help archives, I think this may have something to do with the function (it is not returning a vector but a number), but I don't see exactly what. The function to integrate was defined with a for loop first: for (i in 1:(nbcol-1)) {tdon1<-tdon[,i]

When I run the following function HQ2 <- function(n) { nv <- 6 * sqrt(n) fcn <- function(z) { pchisq(z^2 / 36, n - 1) * dnorm(nv - z) } ## I want the integral from 0 to infinity: f.Inf <- integrate(fcn, 0, Inf) ## Doc: "Don't do this": f.100 <- integrate(fcn, 0, 100) cbind(f.Inf, f.100) } I get, for n = 9 and