binconf() R function from [Hmisc]

Confidence Intervals for Binomial Probabilities

Produces 1-alpha confidence intervals for binomial probabilities.


binconf(x, n, alpha=0.05,
        method=c("wilson","exact","asymptotic","all"),
        include.x=FALSE, include.n=FALSE, return.df=FALSE)

x: vector containing the number of "successes" for binomial variates
n: vector containing the numbers of corresponding observations
alpha: probability of a type I error, so confidence coefficient = 1-alpha
method: character string specifing which method to use. The "all" method only works when x and n are length 1. The "exact" method uses the F distribution to compute exact (based on the binomial cdf) intervals; the "wilson" interval is score-test-based; and the "asymptotic" is the text-book, asymptotic normal interval. Following Agresti and Coull, the Wilson interval is to be preferred and so is the default.
include.x: logical flag to indicate whether x should be included in the returned matrix or data frame
include.n: logical flag to indicate whether n should be included in the returned matrix or data frame
return.df: logical flag to indicate that a data frame rather than a matrix be returned

a matrix or data.frame containing the computed intervals and, optionally, x and n.

Rollin Brant, Modified by Frank Harrell and

Brad Biggerstaff

Centers for Disease Control and Prevention

National Center for Infectious Diseases

Division of Vector-Borne Infectious Diseases

P.O. Box 2087, Fort Collins, CO, 80522-2087, USA

A. Agresti and B.A. Coull, Approximate is better than "exact" for interval estimation of binomial proportions, American Statistician,

52 :119--126, 1998.

R.G. Newcombe, Logit confidence intervals and the inverse sinh transformation, American Statistician,

55 :200--202, 2001.

L.D. Brown, T.T. Cai and A. DasGupta, Interval estimation for a binomial proportion (with discussion), Statistical Science,

16 :101--133, 2001.


binconf(0:10,10,include.x=TRUE,include.n=TRUE)
binconf(46,50,method="all")