similar to: , Exact Confidence Interval

HI All: Does anyone know the code behind the qbeta function in R? I am using it to calculate exact confidence intervals and I am getting 'NaN' at places I shouldnt be. Heres the simple code I am using: k<-3 > x<-NULL > p<-rbeta(k,3,3)# so that the mean nausea rate is alpha/(alpha+beta) > min<-10 > max<-60 > n<-as.integer(runif(3,min,max)) > for(i in

Hi, I am trying to change a SAS macro to R. here is my code. I get an error at the last line. attach(fram) dset1<-cbind(AGE,BMI,DEATH) > BMIGRP<-cut(BMI,breaks=3,right=TRUE) > AGEGRP<-floor(AGE/10)-2 > dset<-cbind(AGEGRP,BMIGRP,DEATH) > maxage<-max(dset[,1]) > minage<-min(dset[,1]) > #maxcls<-dset[,2] > #mincls<-dset[,2] >

Hi: I have asked this question on Cross-Validated. So it might be a cross posting but havent received any responses to it. I am trying to see which distribution will best fit the data I am working on. The dataset is as following: Site Nausea headache Abdominal Distension 1 17 5 10 2 12

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

Full_Name: Ziheng Yang Version: 1.3.1 OS: Windows 98 Submission from: (NULL) (172.136.54.89) I noticed that qbeta is sometimes wrong and the error is not even due to the beta parameters being too extreme. I am calculating the quantiles corresponding to cdf = 0.05, 0.15, ..., 0.95. The value corresponding to cdf=0.25 is wrong while all other values are correct. qbeta(0.05, 0.143891, 0.05) =

I need to run qbeta on a set of 500K different parameter pairs (with a fixed quantile). For most pairs qbeta finds the solution very quickly but for a substantial minority of the cases qbeta is very slow. This occurs when the solution is very close to zero. qbeta is getting answers to a precision of about 16 decimal places. I don't need that accuracy. Is there any way to set the precision of

Hi All: I am trying to generate Beta-Binomial data with regressors using R. I have used the following code to generate Beta-Binomial data. Now I want to add a covariate to the equation. I would then like to use the simulated data to run the Beta-Binomial model with covariates on it. Appreciate any help. set.seed(111) k<-20 n<-60 x<-NULL p<-rbeta(k,3,3)# so that the mean nausea rate

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 there, this is just to let you know that the qbeta function, which was copied from R into Gnumeric, has been confirmed to be miscompiled by gcc 2.96 on Linux. (That's Red Hat's compiler.) This shows by qbeta(0.025,4,0.5) ending up taking the wrong branch of "if (alpha <= 0.5)". We compile things in a different context, so this may or may not affect you. The qbeta

Hi: Does Ellipse and dataellipse function in R produce the same ellipse? I wanted to see how the radius for the Ellipse function in R calculated. Also what is the var-covariance matrix, if any, assumed for the dataellipse function? Heres an example of the code where I am generating Multivariate normal data and creating ellipse using the 2 functions: library(car) library(mvtnorm) mu =

>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

Hi, I cant understand where I am going wrong.Below is my code.I would really appreciate your help. Thanks. > genfile<-read.table("c:/tina/phd/bs871/hw/genfile.txt",skip=1) > > #read in SNP data > snp.dat <- as.matrix(genfile) > snp.name <- scan("c:/tina/phd/bs871/hw/genfile.txt",nline=1,what="character") Read 100 items

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

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

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

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.

Hi: Is there a way we can set up WINBUGS to run 100 simulated datasets on the same model and output results? Or do we have to call in each dataset at a time and repeat the process 100 times manually? Thanks Anamika [[alternative HTML version deleted]]

Hi All: I was looking for some help to code the equation of an ellipse in R, given I have a Bivariate Normal distribution (X,Y) with mean (m1,m2) and var-covariance matrix (var1,cov12,cov12,var2). Thanks Anamika [[alternative HTML version deleted]]

Dear R Users, Is the p-value reported in a two-tailed binomial exact test in error or is it a feature? If it is a feature, could someone provide a reference for its two-tailed p-value computations? Using Blaker's (2000 - Canad. J. Statist 28: 783-798) approach,the p-value is the minimum of the two-tailed probabilities $P \left(Y\geq y_{obs}\right)$ and $P\left(Y\leq y_{obs}\right)$

I'm writing a C program and I'd like to include in it some R routine (quantile functions, typically). I found the functions I have to use (qnorm.c qbeta.c ecc) e some include (nmath.h rmath.h ecc). But I can't compile the project, I receive linker error. Somebody can help me? I say, if I want to include qbeta (for example) in a project of mine, what should I do?