similar to: Sweave data-figure coupling

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 all, I am wondering whether there is a small bug in the binom.test function of the ctest library (I'm using R 1.6.0 on windows 2000, but Splus 2000 seems to have the same behaviour). Or perhaps I've misunderstood something. the command binom.test(11,100,p=0.1) and binom.test(9,100,p=0.1) give different p-values (see below). As 9 and 11 are equidistant from 10, the mean of the

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

Hello, I am trying to use optim() on a function involving a summation. My function basically is a thinned poisson likelihood. I have two parameters and in most cases optim() does a fine job of getting the minima. I am simulating my data based on pre specified parameters, so I know what I should be getting. However when my true parameters fall in a particular range, optim() gives

I am sorry if this question is vague or uninformed. I am just learning R and struggling. I am using the book Hierarchical Modeling and Inference in Ecology and they provide examples of R code. I have the following code from the book but when I run it I don't get any output. I cannot get the values of 'out' to show up. Basically, I just want to see my estimates for b0,

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

Full_Name: Uffe H?gsbro Thygesen Version: 2.2.0 OS: linux Submission from: (NULL) (130.226.135.250) Hello all. pbinom(q=0,size=0,prob=0.5) returns the value NaN. I had expected the result 1. In fact any value for q seems to give an NaN. Note that dbinom(x=0,size=0,prob=0.5) returns the value 1. Cheers, Uffe

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

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

On Mon, Mar 12, 2018 at 2:18 PM, Martin Maechler <maechler at stat.math.ethz.ch> wrote: > [...] > Is that so? Not according to my reading of the 'Writing R > Extensions' manual, nor according to what I have been doing in > all of my packages for ca. 2 years: > > The rule I have in my mind is > > 1) NAMESPACE Import(s|From) \ >

Hi, I've two fitted models, one binomial model with presence-absence data that predicts probability of presence and one gaussian model (normal or log-normal abundances). I would like to evaluate these models not on their capability of adjustment but on their capability of prediction by calculating the (log)likelihood between predicted and observed values for each type of model. I found

Buenas! Supongo que será fácil, pero se me está haciendo difícil. Tengo un documento Rmarkdown en RStudio en el que escribí lo que me pidieron, que me lo piden en formato MS Word. Markdown lo saca decentemente, pero me temo que no tan decente del todo las imágenes. Quiero que, además de salir las imágenes incrustadas en los respectivos chunks, también salve automáticamente la imágen en archivos a

Full_Name: Petr Savicky Version: 2.4.0 OS: Fedora Core release 2 Submission from: (NULL) (62.24.91.47) the error is > binom.test(0.56*10000,10000) Error in binom.test(0.56 * 10000, 10000) : 'x' must be nonnegative and integer while > binom.test(5600,10000) yields correct result. The same error occurrs for > binom.test(0.57*10000,10000)

Hello all, I am trying to understand the different results I am getting from the following 3 commands: chisq.test(c(62,50), p = c(0.512,1-0.512), correct = F) # p-value = 0.3788 binom.test(x=62,n=112, p= 0.512) # p-value = 0.3961 2*(1-pbinom(62,112, .512)) # p-value = 0.329 Well, the binom.test was supposed to be "exact" and give the same results as the pbinom, while the chisq.test

Hello everybody. Does anyone else find the last test in the following sequence odd? Can anyone else reproduce it or is it just me? > binom.test(100,200,0.13)$p.value [1] 2.357325e-36 > binom.test(100,200,0.013)$p.value [1] 6.146546e-131 > binom.test(100,200,0.0013)$p.value [1] 1.973702e-230 > binom.test(100,200,0.00013)$p.value [1] 0.9743334 (R 1.5.1, Linux RedHat 7.1) --

Hi the list, I would need some advice on something that looks like a FAQ: the possibility of providing vectors to optim() function. Here is a stupid and short example summarizing the problem: -------------------------------- example 1 ------------ 8< ---------------------- library(stats4) data <- rnorm(100,0,1) lik1 <- function(m, v, data) { N <- length(data) lik.mean <-

Hi, I noticed this problem on my home desktop running FC4 and again on my laptop running FC5. Both have previously compiled and passed make check-all on 2.3.1 svn revisions from 10 days ago or so. On both these machines, make check-all is consistently failing (4 out of 4 attempts on the FC 4 desktop and 3 out of 3 on the FC 5 laptop) in the p-r-random-tests tests. This is with both default

R. 1.1.0 The example below is self explanatory. ## 1 ## # works fine > binom.test((50*.64),50,.5,alt='g') ... Exact binomial test ... ## 2 ## # WHAT ! ? > binom.test((50*.65),50,.5,alt='g') Error in binom.test((50 * 0.65), 50, 0.5, alt = "g") : x must be an

Hi, I trying to determine the best way to compute the power for a one-sample one-sided binomial test. Specifically I need to sample a population of individuals and ask whether a sample rate of 0% is compatable with a minimum threshold of 3% and how many samples are needed. I have made use of power.prop.test but I am not sure if a) that is the correct (or best) function to use and b) if the