similar to: Proper power computation for one-sided binomial tests.

I wanted to know if there is a way to perform a two sample binomial test in R. I know you can use the proportion test i.e.: prop.test(c(19,5),c(53,39),p=NULL,alternative="two.sided"). But I was looking to use the exact binomial test, binom.test, however when I have tried replacing prop.test with binom.test I get an error. Is there any way to do this? -- View this message in context:

I'm trying to compute sample size requirements for a binomial exact test. we want to show that the proportion is at least 90% assuming that it is 95%, with 80% power so any asymptotic approximations are out of the questions. I was planning on using binom.test to perform the simple test against a prespecified value, but cannot find any functions for computing sample size. do any exist?

hello, i did several binomial tests and noticed for one sparse dataset that binom.test(1,1,0.5) gives a p-value of 1 for the null, what i can't quite grasp. that would say that the a prob of 1/2 has p-value of 0 ?? - i must be wrong but can't figure out the right interpretation.. best, kay ----- ------------------------ Kay Cichini Postgraduate student Institute of Botany Univ. of

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

R-experts: A quick question, please. >From a lab exp, I got 12 positives out of 50. To get 90% CI for this , I think binom.test might be the one to be used. Is there a better way or function to calculate this? > binom.test(x=12, n=50, p=12/50, conf.level = 0.90) Exact binomial test data: 12 and 50 number of successes = 12, number of trials = 50, p-value = 1 alternative

I need to run binomial tests (binom.test) on a large set of data, stored in a table - 600 tests in total. The values of x are stored in a column, as are the values of n. The data for each test are on a separate row. For example: X N 11 19 9 26 13 21 13 27 18 30 It is a two-tailed test, and P in all cases is 0.5. My question is: Is there a quicker way of running these tests without having to

When I perform a two-tailed sign test with the following simple syntax, binom.test(59,100) R returns a P-value (0.088) but nothing else. As I want the result for a one-tailed test I take P/2 = 0.044). However, the journal to which I've submitted my results requests the test statistics, not just the P-values. How can I make R return the test statistics? Best regards, Johan Stenberg, Umea

Hi everyone, Here's how I solved a problem for my stats class. I'm pretty sure I understand what's going on, but I wonder if there's a more direct way to solve it. Problem summary: A recent poll indicated that Candidate A is leading B with 55% of the vote. How many voters need to be surveyed to ensure a margin of error of +/- 2.5% with 99% confidence. Here's what I did:

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 all, A friend just showed me how ks.test fails to work with pbinom for small "size". Example: x<-rbinom(10000,10,0.5) x2<-rbinom(10000,10,0.5) ks.test(x,pbinom,10,0.5) ks.test(x,pbinom,size = 10, prob= 0.5) ks.test(x,x2) The tests gives significant p values, while the x did come from binom with size = 10 prob = 0.5. What test should I use instead ? Thanks, Tal

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 have a seemingly simple proportional test. ?here is the question I am trying to answer: ? There is a test running each day in the lab, the test comes out as either positive or negative. So at the end of each month, we can calculate a positive rate in that month as the proportion of positive test results. The data look like: ? Month??? ??# positive?????? # total tests??? positive rate

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)

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

R 0630 for windows > library(ctest) > binom.test(7,10,p=0.3, alternative="two.sided") returns a p-value of =< 2.2e-016 and a warning In Splus 3.4 > binom.test(7,10,p=0.3, alternative="two.sided") returns a p-value of 0.0106 I think it is the max(v[v<=(1+eps)*PVAL]) causing the problem... max() of an empty vector....... Mai Z

Hi, I want to compute p value of sign test for sample size=15 from normal distr., sd=0.5, mean=1, alternative should be two sided. Is this code correct in this situation? binom.test(sum(rnorm(15,1,0.5)>0),15,p=0.5,alternative="two")$p.value Or should I use another code (function) e.g. rbinom? Thank you very much. kind regards, T. Bal [[alternative HTML version deleted]]

Hullo, Can someone suggest whether the binomial test as described in the link http://home.clara.net/sisa/binomial.htm is available in an equivalent form in R? I have downloaded the R package from the CRAN site. Using R will help me do this test rapidly Many Thanks Ramachandran Dr. S. Ramachandran Scientist E I G.N. Ramachandran Knowledge Centre for Genome Informatics Institute of Genomics and

Dear All, I am trying to fit some data both as a negative binomial and a zero inflated binomial. For the first case, I have no particular problems, see the small snippet below library(MASS) #a basic R library set.seed(123) #to have reproducible results x4 <- rnegbin(500, mu = 5, theta = 4) #Now fit and check that we get the right parameters fd <- fitdistr(x4, "Negative

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)

The only references to this I can find searching the archives are to a student who asked in relation to his course work on a stats course. Promise I'm not doing that! I have a situation in which we want to test proportions against an expected proportion, binom.test() is great. I'd like to do some post hoc power tests (the x and n were beyond our control in the survey as all we could set