Hmisc (version 4.5-0)

binconf: Confidence Intervals for Binomial Probabilities

Description

Produces 1-alpha confidence intervals for binomial probabilities.

Usage

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

Arguments

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

Value

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

References

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.

Examples

# NOT RUN {
binconf(0:10,10,include.x=TRUE,include.n=TRUE)
binconf(46,50,method="all")
# }