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 |
include.n |
logical flag to indicate whether |
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
bkb5@cdc.gov
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")
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