similar to: Exact Binomial test feature or bug?

Dear Spencer Thank you for this extensive explanation of the problem. I was just curious. Best regards Christian ============================================== Christian Stratowa, PhD Boehringer Ingelheim Austria Dept NCE Lead Discovery - Bioinformatics Dr. Boehringergasse 5-11 A-1121 Vienna, Austria Tel.: ++43-1-80105-2470 Fax: ++43-1-80105-2683 email: christian.stratowa at

I am trying to find a definition for the two-tailed exact binomial test but have been unsuccessful. Can you help? --------------------------------- [[alternative HTML version deleted]]

Hello, I wuold like to propose my modifications of the original cor.test to you : I tried to calcolate the correct p-value for Spearman and Kendall's test with ties. Let me know what you think. Thanks you for your time. Antonietta di Salvatore test <- function(x, ...) UseMethod("test") test.default <- function(x, y, alternative = c("two.sided",

I am trying to do a repeated measurement anova using an lme. I have the following variables: -ID, the identification of the individual -trail, with 2 levels -treatment, with 3 -time, measure 5 times the same individual -VCL, the response variable I tried the following in R, within.gr<-groupedData(VCL~time|ID/treatment/time,data=within)

Hi all, I have just uploaded a new package to the ftp site called exact2x2. The package does exact conditional tests for 2x2 tables. Specifically, it does Fisher's exact test and Blaker's exact test (see Blaker, 2000, Canadian Journal of Statistics, 783-798). The motivation for the package is to have inferences from p-values and confidence intervals match as much as is possible for the

Hi all, I have just uploaded a new package to the ftp site called exact2x2. The package does exact conditional tests for 2x2 tables. Specifically, it does Fisher's exact test and Blaker's exact test (see Blaker, 2000, Canadian Journal of Statistics, 783-798). The motivation for the package is to have inferences from p-values and confidence intervals match as much as is possible for the

Hi, When writing formulae in Rd files I use \leq to get a less-than-or-equal-to sign, as this is what the LaTeX references I consulted suggested. This works correctly for the PDF output (Rcmd Rd2dvi.sh --pdf), however when the Rd file is converted to text or html this command is converted to "<=q". A workaround is to use \le instead of \leq which works with my LaTeX distribution

Hello At the start of October, Duane Johnson announced the Query mixin plugin on this list. The code was attached to the announcement email. Unfortunately, I''ve not been able to locate the attached code. Goggle has not helped me this time. Could somebody forward it to me? Thanks in advance Harvey This e-mail has been scanned for all viruses by MessageLabs.

As a very new user of R, and a user of S with very limited experience, I thought I would start humbly with a simple t.test. The response to the command sequence sample <- scan() 4 5 6 7 8 9 t.test(sample,mu=20) rather surprised me, because of the idiosyncratic choice of a confidence interval for the mean of the population from which the sample was drawn. I asked my colleague Bob Henery

Hello, I'm looking to calculate a 95% confidence interval about my estimate for a sample's weighted mean, where the calculated confidence interval would equal the t-test confidence interval of the sample in the case when all of the weights are equal. My initial thought was to simply implement a modified version of the t-test function but substituting the weighted variance and mean for the

Given a vector : pvec=(p1,p2,.... p J) with sum(pvec)=1, all the elements are non-negative, that is, they are probabilities a matrix A ( N* J ), with the elements alpha(ij) are 0 or 1 I want to MAXIMIZE THE RESULT RESULT= product( i=1, to N [ sum ( alpha(ij)* pj , j =1,to J ) ] ) thus, I need to get pvec. how should I do ? for example

Btw, all the pbinom() examples posted to S-news today return the proper result in R Martin >>>>> "JS" == James Stapleton <stapleton at stt.msu.edu> writes to S-news : JS> More interesting behavior of pbinom: >> pbinom(3,2,.8) JS> [1] 1 >> pbinom(30,2,.8) JS> [1] 0.9967653 >> pbinom(40,2,.8) JS> [1]

In pvec(list(1, 2), FUN, mc.cores=2) FUN sees integer() arguments whereas pvec(list(1, 2, 3), FUN, mc.cores=2) FUN sees list() arguments; the latter seems consistent with pvec's description. This came up in a complicated Bioconductor thread about generics and parallel evaluation https://stat.ethz.ch/pipermail/bioc-devel/2012-October/003745.html One relevant point is that a

This came up due to a question from Anders Hald: Bernoulli calculated an approximation to the smallest n so that P(0.58 <= x/n <= 0.62) >= 1000/1001 What is the exact value? Now try n <- 6350:6500 Pr <- function(n)pbinom(0.62*n,n,0.6) - pbinom(0.58*n,n,0.6) + dbinom(0.58*n,n,0.6) plot(n,Pr(n),type="b") abline(h=1000/1001) min(n[Pr(n)>1000/1001]) Next, try Pr

Dear R list members, I'd like to make a graph of coefficients of the intercept, variable 1, and variable 2 (and possibly the interaction between variable 1 and variable 2). When I use the lmList function as attached below, it shows a nice coefficient graph. > PACRP.lis <- lmList(PAffect ~ CRPC + CRPT + CINT | ID, redinteract) > coef(PACRP.lis) > PACRPlis.coef <-

> From: "Alan T. Arnholt" <arnholt at math.appstate.edu> > To: Bill Venables <William.Venables at cmis.CSIRO.AU> > Cc: r-help at stat.math.ethz.ch, arnholt at math.appstate.edu > Subject: Re: [R] Lack of Fit test > Date: Wed, 23 Feb 2000 09:40:21 -0500 (EST) > X-Authentication: none > > > I guess my question was not adequately stated when I sent

Dear R users, I?m trying to optimize simultaneously two binomials inequalities (used in acceptance sampling) which are nonlinear solution, so there is no simple direct solution. Please, let me explain shortly the the problem and the question as following. The objective is to obtain the smallest value of 'n' (sample size) satisfying both inequalities: (1-alpha) <= pbinom(c, n, p1)

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)

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 am trying to calculate Bayesian Credible Intervals for a proportion (disease prevalence values to be more specific) and am having trouble using R to do this. I am working with ncredint() function but have not had success with it. Please help! Example: Positive samples = 3 Total sampled = 10 Prevalence = 0.3 pvec <- seq(1,10,by=1) npost = dbinom(pvec,10,prob=0.3, log=FALSE) ncredint(pvec,