similar to: pnbinom.c qnorm.c

Thanks Ben for verifying the issue. It is always reassuring to hear when others can reproduce the problem. I wrote a small patch that fixes the issue (https://github.com/r-devel/r-svn/pull/11): diff --git a/src/nmath/qnbinom.c b/src/nmath/qnbinom.c index b313ce56b2..d2e8d98759 100644 --- a/src/nmath/qnbinom.c +++ b/src/nmath/qnbinom.c @@ -104,6 +104,7 @@ double qnbinom(double p, double size,

Hi all, I recently noticed that `qnbinom()` can take a long time to calculate a result if the `size` argument is very small. For example qnbinom(0.5, mu = 3, size = 1e-10) takes ~30 seconds on my computer. I used gdb to step through the qnbinom.c implementation and noticed that in line 106 (https://github.com/wch/r-source/blob/f8d4d7d48051860cc695b99db9be9cf439aee743/src/nmath/qnbinom.c#L106)

This came up on r-help but indicates a bug. dnbinom(x,n,p) calls dbinom_raw(n-1,...) which returns 0 for n<1. -thomas ---------- Forwarded message ---------- Date: Thu, 08 Feb 2001 17:10:23 +0000 From: Yudi Pawitan <yudi@stat.ucc.ie> To: Mark Myatt <mark@myatt.demon.co.uk> Cc: R-Help <r-help@stat.math.ethz.ch> Subject: Re: [R] Goodness of fit to Poisson / NegBinomial

Cordial saludo para cada uno. De manera amable les pido ayuda para acceder al código R usado para el algoritmo de la función qnorm. Gracias por su ayuda. César Escalante C. [[alternative HTML version deleted]]

Full_Name: Xiong Guanglei Version: 1.8.1 OS: Linux Submission from: (NULL) (202.38.103.50) qnorm(x) when x>1.0

how R implement qnorm() I wonder anyone knows the mathematical process that R calculated the quantile? The reason I asked is soly by curiosity. I know the probability of a normal distribution is calculated through integrate the Gaussian function, which can be implemented easily (see code), while the calculation of quantile (or Zα) in R is a bit confusing as it requires inverse error function (X

At the top of src/nmath/qnorm.c it is stated: * Copyright (C) 1998 Ross Ihaka * Copyright (C) 2000--2005 The R Development Core Team * based on AS 111 (C) 1977 Royal Statistical Society * and on AS 241 (C) 1988 Royal Statistical Society The routine is in fact an f2c'd version of AS241 from StatLib: http://lib.stat.cmu.edu/apstat/241 and http://lib.stat.cmu.edu/apstat/ It

(1) ...need to speed up a monte-carlo sampling...any suggestions about how I can get R to use all 8 cores of a mac pro would be most useful and very appreciated... (2) spent the last few hours trying to get pnmath to compile under os- x 10.5.4... using gcc version 4.2.1 (Apple Inc. build 5553) as downloaded from CRAN, xcode 3.0... ...xcode 3.1 installed over top of above after

Hi Martin, thanks for verifying. I agree that the Cornish-Fisher seems to struggle with the small size parameters, but I also don't have a good idea how to replace it. But I think fixing do_search() is possible: I think the problem is that when searching to the left y is decremented only if `pnbinom(y - incr, n, pr, /*l._t.*/TRUE, /*log_p*/FALSE)) < p` is FALSE. I think the solution is

>>>>> Constantin Ahlmann-Eltze via R-devel >>>>> on Mon, 10 Aug 2020 10:05:36 +0200 writes: > Thanks Ben for verifying the issue. It is always reassuring to hear > when others can reproduce the problem. > I wrote a small patch that fixes the issue > (https://github.com/r-devel/r-svn/pull/11): > diff --git

Dear list, I ran into a strange behaviour of the pnbinom function - or maybe I just made a stupid mistake. First thing is that pnbinom seems to be very slow. The other - more interesting one - is that I get two different curves when I plot the estimated density and the density given by pnbinom. Shouldn't it be the same? This is only the case, I think, if I use the parameter size = 1. I

Try changing your method to "ML" and try again. I tried the run the first example from the documentation and it failed with the same error. Changing the estimation method to ML worked. @List: Can anyone else verify the error I got? I literally ran the following two lines interactively from the example for goodfit: dummy <- rnbinom(200, size = 1.5, prob = 0.8) gf <- goodfit(dummy,

I found the following strange behavior using qnorm() and pnorm(): > x<-8.21;x-qnorm(pnorm(x)) [1] 0.0004638484 > x<-8.22;x-qnorm(pnorm(x)) [1] 0.01046385 > x<-8.23;x-qnorm(pnorm(x)) [1] 0.02046385 > x<-8.24;x-qnorm(pnorm(x)) [1] 0.03046385 > x<-8.25;x-qnorm(pnorm(x)) [1] 0.04046385 > x<-8.26;x-qnorm(pnorm(x)) [1] 0.05046385 > x<-8.27;x-qnorm(pnorm(x))

Hi everyone, I have the following problem. I have some small p-values but when I use qnorm(1-4e-30) I get an error. Is there anyway to get around this? -- Thanks, Jim. [[alternative HTML version deleted]]

On a recent FreeBSD 8.0-CURRENT (i386) building R (any version) breaks with the following messages: ---------------------------------------------------------------------- [...snip...] gcc -std=gnu99 -I. -I../../src/include -I../../src/include -I/usr/local/include -DHAVE_CONFIG_H -g -O2 -c wilcox.c -o wilcox.o gcc -std=gnu99 -I. -I../../src/include -I../../src/include -I/usr/local/include

Hello All I am looking for high precision values for the normal distribution in the tail,(1e-10 and 1 - 1e-10) as the R package that I am using sets any number which is out of this range to these values and then calls the qnorm and qt function. What I have noticed is that the qnorm implementation in R is not symmetric when looking at the tails. This is quite surprising to me, as it is well known

Hello all, I have a question about basic statistics. Given a PDF value of 0.328161, how can I find out the value of -0.625 in R? It is like reversing the dnorm function but I do not know how to do it in R. > pdf.xb <- dnorm(-0.625) > pdf.xb [1] 0.328161 > qnorm(pdf.xb) [1] -0.444997 > pnorm(pdf.xb) [1] 0.628605 Many thanks, Edwin -- View this message in context:

dear all, it will just take you a minute to tell me the difference between qnorm and qqnorm. are they same or is there any difference between them?? regards

I've recently tried to get 0.62.3 up on our Suns (4.1.4, using gcc; I'm also having trouble with a Solaris 2.6 compile but I haven't given up hope on that one yet) -- the last compile I did was 0.61.1, which worked smoothly. Running ./configure --prefix=/usr/local/apps/R/R-0.62.3 [--with-g77] (tried both with and without g77, eventually get to the same place), I get a conflict

I am using pnorm() with the log.p=T argument to get approximations to ln \Phi(x) and qnorm with the log.p=T argument to get estimates of \Phi^{-1}(exp(x)). What approximations are used in these two functions (I noticed in the source pnorm.c it doesn't look like Abramowitz and Stegen) and where can I find the citation? Thanks, Richard Morey