similar to: confidence interval given by prop.test()

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

The confidence interval calculation in prop.test appears to be incorrect when alternative="greater". The upper limit is always set to 1.000. Am I missing something? > total=c(250,250) > success=c(55,31) > prop.test(success,total,alternative="greater",correct=TRUE) 2-sample test for equality of proportions with continuity correction data: success out of

Full_Name: Richard Johnston Version: 2.4.0 OS: OS X Submission from: (NULL) (69.169.0.241) The confidence interval calculation for prop.test appears incorrect when alternative="greater" . The upper limit is always set to 1.0000. The lower limit appears to be correct. > total=c(250,250) > success=c(55,31) >

Dear R-help, I'm using prop.test() to compute a confidence interval for a proportion under R version 2.4.1, as in: prop.test(x = 340, n = 400)$conf [1] 0.8103309 0.8827749 I have two questions: 1) from the source code my understanding is that the confidence interval is computed according to Wilson, E.B. (1927) Probable inference, the law of succession, and statistical inference. J. Am.

Hello, Does anyone know which method from Newcombe (1998)* is implemented in prop.test for comparing two proportions? I would guess it is the method based on the Wilson score (for single proportion), with and without continuity correction for prop.test(..., correct=FALSE) and prop.test(..., correct=TRUE). These methods would correspond to no. 10 and 11 tested in Newcombe, respectively. Can

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

Dear Josh, Thanks for your help! Does your answer mean, that you agree the two methods should do the same, and what I was guessing, despite the small differences? What I prefer about ci.pd is, that the help clearly says which method is implemented, which is not the case for prop.test. But I do not know who has programmed the function. Best wishes Steffi Stefanie von Felten, PhD Statistician

Hi, This code: n=40 x=17 phat=x/n SE=sqrt(phat*(1-phat)/n) zstar=qnorm(0.995) E=zstar*SE phat+c(-E,E) Gives this result: [1] 0.2236668 0.6263332 The TI Graphing calculator gives the same result. Whereas this test: prop.test(x,n,conf.level=0.99,correct=FALSE) Give this result: 0.2489036 0.6224374 I'm wondering why there is a difference. D. -- View this message in context:

Hi all, Presuming that my reply on r-help this morning was correct, attached is a patch file against the current svn trunk version of prop.test.Rd to add the references for the methods. Any corrections are welcome. Regards, Marc Schwartz

I have just joined this list (and just started using R), so please excuse any etiquette breaches as I do not yet have a feel for how the list operates. I am in the process of teaching myself statistics using R as my utility as my ultimate goals cannot be satisfied by Excel or any of the plug-ins I could afford. I am currently looking at chap12 page 552 of Weiss's Introductory Statistics

All, How come both of these are the same. Both say "1-sample proportions test without continuity correction." I would suspect one would say "without" and one would say "with." > prop.test(118,236,.5,correct=FALSE,conf.level=0.95) 1-sample proportions test without continuity correction data: 118 out of 236, null probability 0.5 X-squared = 0, df = 1,

Hi everyone, Suppose I have a count the occurrences of positive results, and the total number of occurrences: pos <- 14 total <- 15 testing that the proportion of positive occurrences is greater than 0.5 gives a p-value and confidence interval: prop.test( pos, total, p=0.5, alternative='greater') 1-sample proportions test with continuity correction data: 14 out of

To the R-help list, In the documentation of the prop.trend.test function in the stats package, no bibliography has been provided which would allow one to find out the theoretical basis of that function and/or details of its implementation. May I suggest that some bibliography be included, as it generally happens with other statistical functions. I currently use R 2.8.0 running on Windows XP.

Preparing a paper for a medical journal. Using the prop.test() function in R (v2.4.0) to compare two groups' response to data like the following. A sample of 100 individuals from Population I, 18 with positive readings from a certain test, vs. A sample of 148 individuals from Population II, 61 with positive readings. Results look like this: R version 2.4.0 Patched (2006-11-25

I have the clinical study data. Year 0 Year 3 Retinol (nmol/L) N Mean +-sd Mean +-sd Vitamin A group 73 1.89+-0.36 2.06+-0.53 Trace group 57 1.83+-0.31 1.78+-0.30 where N is the number of male for the clinical study. I want to test if the mean serum retinol has increased over 3 years among subjects in the vitamin A group. > 1.89+0.36

from revision 51769: Index: prop.test.Rd =================================================================== --- prop.test.Rd (revision 51769) +++ prop.test.Rd (working copy) @@ -60,7 +60,7 @@ If there is only one group, then the null tested is that the underlying probability of success is \code{p}, or .5 if \code{p} is - not given. The alternative is that the probability of success if

Hello, is there anyone who has some ideas about the problem I posted? Help! [[alternative HTML version deleted]]

Hi, I am trying to sort out a discrepancy between power calculations results between me and another statistician. I use R but I am not sure what she uses. It is on the proportions test and so I have been using pwr.prop.test. I think I have tracked the problem down to pwr.prop.test not using the continuity correction for the test (I did this by using the java applet from

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

I have a simple table below called temptable and i want to obtain the same structure that prop.table creates except get the counts rather than the proportions. margin.table seems to create one table with columns and rows whereas I am looking for the three table type structure that prop.table gives. Thanks. temptable<-structure(c(0L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,