similar to: how to compute highest density interval?

(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, i'm new to R and i'm trying to port a quattro pro spreadsheet into R. spreadsheets have optional lower and upper limit parameters on the beta distribution function. i would like to know how to incorporate this with R's pbeta function. thanks in advance, mara. ____________________________________________________________________________________ Park yourself in front of a

Given that a number of us are housebound, it might be a good time to try to improve the approximation. It's not an area where I have much expertise, but in looking at the qbeta.c code I see a lot of root-finding, where I do have some background. However, I'm very reluctant to work alone on this, and will ask interested others to email off-list. If there are others, I'll report back.

Hi, I want to generate random samples from truncated normal say Normal(0,1)Indicator((0,1),(2,4)). It has more than one intervals. In the library msm, it seems to me that the 'lower' and 'upper' arguments can only be a number. I tried rtnorm(1,mean=0,sd=1, lower=c(0,2),upper=c(1,4)) and it didn't work. Can you tell me how I can do truncated normal at more than one intervals?

There is a problem with `rpois'. It does seem to take care about the order of the arguments. This is an example: > rpois(n=1,lambda=2) [1] 3 > rpois(lambda=2,n=1) [1] 2 0 It obviously uses the first argument as the number of samples to be drawn, which is wrong. I used Version 0.49 Beta (April 23, 1997). Fredrik

There is a problem with `rpois'. It does seem to take care about the order of the arguments. This is an example: > rpois(n=1,lambda=2) [1] 3 > rpois(lambda=2,n=1) [1] 2 0 It obviously uses the first argument as the number of samples to be drawn, which is wrong. I used Version 0.49 Beta (April 23, 1997). Fredrik

Despite the need to focus on pbeta, I'm still willing to put in some effort. But I find it really helps to have 2-3 others involved, since the questions back and forth keep matters moving forward. Volunteers? Thanks to Martin for detailed comments. JN On 2020-03-26 10:34 a.m., Martin Maechler wrote: >>>>>> J C Nash >>>>>> on Thu, 26 Mar 2020

I've discovered an infelicity (I guess) in qbeta(): it's not a bug, since there's a clear warning about lack of convergence of the numerical algorithm ("full precision may not have been achieved"). I can work around this, but I'm curious why it happens and whether there's a better workaround -- it doesn't seem to be in a particularly extreme corner of parameter

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

>Date: Fri, 2 May 2003 10:03:23 -0400 (EDT) From: Morten Welinder <welinder@rentec.com> >To: p.dalgaard@biostat.ku.dk >CC: r-devel@stat.math.ethz.ch, R-bugs@biostat.ku.dk >Subject: Re: [Rd] qbeta hang (PR#2894) > >Ok, I can confirm that it does not, in fact, loop forever. Just a close >approximation. ... >There are lots of other places that worry me with respect to

Dear All, There is one problem I encountered when I used ADAPT to compute some 2-D integral w.r.t beta density. For example, when I try to run the following comments: fun2<-function(theta){return(dbeta(theta[1],0.005,0.005)*dbeta(theta[2],0.005,0.005))} int.fun2<-adapt(ndim=2,lo = c(0,0), up = c(1,1),functn = fun2,eps = 1e-4) It seems it will take very long time to run. Acturally, I

Hi, I guess that my problem has an obvious answer, but I have not been able to find it. Suppose I create a custom function, consisting of two beta-distributions: myfunction <- function(x) { dbeta(x,2,6) + dbeta(x,6,2) } How can I calculate the quantiles of myfunction? I have not seen any continous function treated in the docs, and applying the "quantile function" gives me an

Full_Name: Morten Welinder Version: 1.6.1 OS: Solaris/sparc Submission from: (NULL) (65.213.85.144) qbeta(0.1, 1e-8, 0.5, TRUE, FALSE) seems to hang for me.

Dear friends. Is this as expected ? Is alpha and beta too large simply ? > dbeta(.1,534,646) [1] NaN Warning message: NaNs produced in: dbeta(x, shape1, shape2, log) Best wishes Troels -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Hi, I sometimes play around with extreme parameters for distributions and found that qbeta is not always monotone as the following example shows. I don't know whether this is serious enough to submit a bug report (as this example is near to the limitations of floating point arithmetic). Josef > x <- qbeta((0:100)/100,0.01,5) > x [1] 0.000000e+00 1.253990e-201 1.589622e-171

>>>>> "TL" == Thomas Lumley <thomas@biostat.washington.edu> writes: TL> On Thu, 24 Aug 2000, Troels Ring wrote: >> Dear friends. >> >> Is this as expected ? Is alpha and beta too large simply ? >> >> > dbeta(.1,534,646) >> [1] NaN >> Warning message: >> NaNs produced in:

The real problem is that pbeta can take forever. That's bug #7153 and a fix is within reach. Morten

make check of R-alpha_2006-04-08_r37675 fails on Debian GNU/Linux 3.1 running on an Intel P4 computer. > version _ platform i686-pc-linux-gnu arch i686 os linux-gnu system i686, linux-gnu status

Hi all! I'm new to this forum so please excuse me if I don't conform perfectly to the protocols on this board! I'm trying to get an estimate of true prevalence based upon results from an imperfect test. I have various estimates of se/sp which could inform my priors (at least upper and lower limits even if with a uniform distribution) and found the following code on this website..

On 23/02/05 I suggested that given R had included TOMS 708 to correct for t= he=20 poor performance of pbeta, TOMS 654 should be included to fix all the pgamm= a=20 problems. I was slightly surprised to find Morten's code had been included= =20 instead 2 days later. I noticed but did not worry that the reference to me = had=20 been removed.=20 The derivation of the asymptotic expansion for