similar to: matched pair proportion test

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, 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

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:

Dear R users this may be a simple question - but i would appreciate any thoughts does anyone know how you would get one lower and one upper confidence interval for a set of data that consists of proportions. i.e. taking a usual confidence interval for normal data would result in the lower confidence interval being negative - which is not possible given the data (which is constrained between

Full_Name: Robert W. Baer, Ph.D. Version: 1.6.2 OS: Windows 2000 Submission from: (NULL) (198.209.172.106) Problem: prop.test() does not seem to produce appropriate confidence intervals for the case where the vector length of x and n is one. (I am not certain about higher vector lengths.) As an example, I include x=6 and n=42 which has a mean proportion of 0.115. When I calculate the 95% CI

Hi, R users, I am trying to use glm to do logistic regression. I know generally when I have two covariates, say x1 and x2, then I do fit <- glm(y~x1+x2,famliy='binomial') But now my covariates form a n*p matrix, say x, so actually each column is a covariate. So I think I should do fit <- glm(y~x,family='binomial') Then I need to predict new data. How should I write the

Hi All, How can I get the expected value from a discrete cdf? Is there any R function that can do this? Thanks, Annie [[alternative HTML version deleted]]

> Dear R-users! > > I am using R 1.0.0 and Windows NT 4.0. > Suppose I have a population of N=100 subjects, a binomial variable and a random sample of n=20 subjects from my population, giving 15 "successes". I am interested in obtaining a confidence interval for the proportion of "successes" in my population. In R, I can use > library(ctest) >

Hi, I am wondering if anyone can suggest how to test the equality of 2 proportions. The caveat here is that the 2 proportions were calculated from the same number of samples using 2 different tests. So essentially we are comparing 2 accuracy rates from same, say 100, samples. I think this is like a paired test, but don't know if really we need to consider the "paired" nature of the

Dear list members, I want to perform in R the analysis "simultaneous confidence interval for multiple proportions", as illustrated in the article of Agresti et al. (2008) "Simultaneous confidence intervals for comparing binomial parameter", Biometrics 64, 1270-1275. If I am not wrong the R function implementing the Agresti et al. method is prop.test(). I ask an help because I

Dear R users, Is it possible to use a generalized linear model to do a binomial comparison of one list of proportions with a matched list of proportions to test for a difference? So, for example: list 1 list 2 a1 | b1 a2 | b2 3 | 4 7 | 9 6 | 7 5 | 1 9 | 1 3 | 1 I want to compare list 1 with list 2 and the samples

Is there any package available in R to do the following hypothesis tests? Testing the means of two Poissons (equivalent to the difference of two proportions) Testing the equality of two proportions from binomials Testing the equality of proprtions of two negative binomials (both conditional and unconditional tests). No large sample approximation tests...I need exact tests Thanks, Jim

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

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

Hi, All, How can I get the indices of the minimum elements in a matrix without using a loop? For example, if the matrix is 4 5 2 2 8 9 5 2 3 Then I want to output (1,3), (2,1), (3,2). Thanks, Annie [[alternative HTML version deleted]]

Hallo! Does anybody know how to calculate a sample size estimation for proportions with continuity correction? ? I only found EpiR which seems to calculate without continuity correction: library(epiR) epi.studysize(treat = .65, control = .50, n = NA, sigma = NA, power = 0.80, r = 1, conf.level = 0.95, sided.test = 2, method = "cohort") $n [1] 340 Thanks! Karl

Hello, I am looking for a sample size function for samples sizes, to test proportions that are not binomial proportions. The proportions represent a ratio of (final measure) / (baseline measure) on the same experimental unit. Searches using RSeek and such bring multiple hits for binomial proportions, but that doesn't seem to fit my situation. Perhaps there's some standard terminology

Hi All, I have a 2-dim density defined on 0<x<1, 0<y<1, x<y. I know the exact formula of the density. How can I visualize it? What plot functions can I use? Thanks, Annie [[alternative HTML version deleted]]

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?

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