similar to: i param in "for" loop does not takes zeros?

Thank you very much for your reply. Your code work well with this example. I modified a little to fit my real data, I got an error massage. Error in split.default(x = seq_len(nrow(x)), f = f, drop = drop, ...) : Group length is 0 but data length > 0 On Thu, Jan 31, 2013 at 12:21 PM, arun kirshna [via R] < ml-node+s789695n4657196h87@n4.nabble.com> wrote: > Hi, > Try this: >

Hi Folks, The recent correspondence about "strange fisher.test result", and especially Peter Dalgaard's reply on Tue 03 April 2007 (which I want to investigate further) led me to take a close look at the code for binom.test(). I now have a query! The code for the two-sided case computes the p-value as follows: if (p == 0) (x == 0) else if (p == 1) (x == n)

Hi Folks, There has been past correspondence regarding Catherine Loader's Bell Labs (oops, Lucent) paper "Fast and Accurate Computation of Binomial Probabilities" which gives the algorithm on which R's dbinom() is based. The original URL given in the R documentation "?dbinom" is: http://cm.bell-labs.com/cm/ms/departments/sia/catherine/dbinom but this link is

Hi Guy's I was wondering if someone could point me in the right direction. dbinom(10,1,0.25) I am using dbinom(10,1,0.25) to calculate the probabilty of 10 judges choosing a certain brand x times. I was wondering how I would go about simulating 1000 trials of each x value ? regards Brendan -- View this message in context:

http://en.wikipedia.org/wiki/Binomial_distribution Bino Hi there, How can I get the legend in the probability density plot in that http://en.wikipedia.org/wiki/File:Binomial_distribution_pmf.svg wiki page ? and the cumalative plot as well? ==================================== # Binomial Distribution x=0:40

Dear people, I'm trying to do some analysis of a data using the models by Royle & Donazio in their fantastic book, particular the following function: http://www.mbr-pwrc.usgs.gov/pubanalysis/roylebook/panel4pt1.fn that applied to my data and in the console is as follows: > `desman.y` <- structure(c(3L,4L,3L,2L,1L), .Names = c("1", "2", "3",

I believe the binom.test procedure is producing one tailed p values rather than the two tailed value implied by the alternative hypothesis language. A textbook and SAS both show 2*9.94e-07 = 1.988e-06 as the two tailed value. As does the R summation syntax from R below. It looks to me like the alternative hypothesis language should be revised to something like " ... greater than or equal

Hi, With the following Sweave minimal file: ---<--------------------cut here---------------start------------------->--- \documentclass{article} \usepackage{Sweave} \begin{document} <<binom-sim>>= thetas <- seq(0, 1, by=0.001) prior <- rep(1, length(thetas)) / length(thetas) lik <- dbinom(1, 1, thetas) lik.p <- prior * lik post <- lik.p / sum(lik.p)

Full_Name: Chanseok Park Version: R 2.2.1 OS: RedHat EL4 Submission from: (NULL) (130.127.112.89) pbinom(any negative value, size, prob) should be zero. But I got the following results. I mean, if a negative value is close to zero, then pbinom() calculate pbinom(0, size, prob). dbinom() also behaves similarly. > pbinom( -2.220446e-22, 3,.1) [1] 0.729 > pbinom( -2.220446e-8, 3,.1)

Hi all, I have some problems with the mle2 function > RogersIIbinom <- function(N0,attackR3_B,Th3_B) {N0-lambertW(attackR3_B*Th3_B*N0*exp(-attackR3_B*(24-Th3_B*N0)))/(attackR3_B*Th3_B)} > RogersII_B <- mle2(FR~dbinom(size=N0,prob=RogersIIbinom(N0,attackR3_B,Th3_B)/N0),start=list(attackR3_B=1.5,Th3_B=0.04),method="Nelder-Mead",data=dat) Error in dbinom(x, size, prob, log)

In principle a good idea, but I'm not sure the whereabouts of Catherine Loader are known at this point. Last peeps from her on the net seem to be about a decade old. .pd > On 18 May 2020, at 10:31 , Abby Spurdle <spurdle.a at gmail.com> wrote: > > This has come up before. > > Here's the last time: > https://stat.ethz.ch/pipermail/r-devel/2019-March/077478.html

FWIW the link from ?dbinom to the Loader paper on Binomials is broken but the paper seems to be available here: https://octave.1599824.n4.nabble.com/attachment/3829107/0/loader2000Fast.pdf Roger Koenker r.koenker at ucl.ac.uk<mailto:r.koenker at ucl.ac.uk> Honorary Professor of Economics Department of Economics, UCL Emeritus Professor of Economics and Statistics, UIUC [[alternative

Hello, With the new version 2.2.0, I get strange plots when using the histogram-like option in plot(). For example, a plot of binomial probabilities: > plot(0:10,dbinom(0:10,10,.1), type = "h", lwd = 30) gives me weird fat cirular bars, with mass out at values with low probability. What is the issue here? This never happened with earlier versions. R on Windows XP SP 2 Intel

Dear friends. In Carlin and Louis "Bayes and emperical Bayes methods.." 1996 the classical example of 12 independent tosses of a fair coin producing 9 heads and 3 tails is given. If the situation is seen as a fixed sample of 12, a binomial lieklihood is used, and Carlin et al reports a probability of 0.075. Using sum(dbinom(9:12,12,.5)) I obtain 0.073 Likewise, if the experiment is

Thank you so much for your help. The function "dbinom" seems to work very well. However, I'm a bit lost with the "dnorm" function. Apparently, I have to compute the mean "mu" and the standard deviation "sd" but what does it mean exactly? I only have a vector of predicted response and a vector of observed response that I would like to compare! What

Hi, Infinite recursion in S3 methods seem to crash R on Windows 2000 (R terminating with the ("Rgui.exe has generated errors...") message, rather than throwing an error. This happens with both Rgui and Rterm. The following toy example triggers this: myf <- function(x, ...) UseMethod("myf") myf.default <- function(x, ...) myf(x) myf(1) ...R crashes... Which I

My interpretation of dput.Rd is that dput() gives an exact ASCII form of the internal representation of an R object. But: rhankin at cuttlefish:~ $ R --version R version 3.6.2 (2019-12-12) -- "Dark and Stormy Night" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) [snip] rhankin at cuttlefish:~ $ R --vanilla --quiet > x <-

Hi, this is my first time using R. I want to simulate the following process: "in a population of size N, there are i individuals bearing genotype A, the number of those bearing A is j in the next generation, which following a binominal distribution (choose j from 2*N, the p is i/2*N), to plot the probability of the next generations, my script is as follows. It cannot run successfully,

(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

Dear all, In order to be sure I understand glmm correctly, I try to reproduce by hand a simple result. Here is a reproducible code. The questions are in _________________ Of course I have tried to find the solution using internet but I was not able to find a solution. I have also tried to follow glmer but it is very complicated code! Thanks for any help. Marc # Generate set of df with nb