binom.test
You can also plot an uncertainty distribution of p, using an uninformed prior (uniform(0,1)), using beta(s+1, n-s+1) i.e. x <- seq(0.091, 0.469, length=100) plot(x, dbeta(.x, shape1=13, shape2=39), xlab="x", ylab="Density", main="Uncertainy distribution for p: beta(a = 12+1, b = 50-12+1)", type="l") Cheers, Francisco Dr. Francisco J. Zagmutt College of Veterinary Medicine and Biomedical Sciences Colorado State University
From: Chuck Cleland <ccleland at optonline.net> To: Ethan Johnsons <ethan.johnsons at gmail.com> CC: r-help at stat.math.ethz.ch Subject: Re: [R] binom.test Date: Thu, 19 Oct 2006 11:27:35 -0400 Ethan Johnsons wrote:
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 hypothesis: true probability of success is not equal to 0.24
90 percent confidence interval:
0.1447182 0.3596557
sample estimates:
probability of success
0.24
You might consider binconf() in the Hmisc package too:
library(Hmisc)
binconf(12, 50, method="all")
PointEst Lower Upper
Exact 0.24 0.130610 0.381691
Wilson 0.24 0.142974 0.374127
Asymptotic 0.24 0.121621 0.358379
thx much ej
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