binom (version 1.0-3)

binom.confint: Binomial confidence intervals

Description

Uses eight different methods to obtain a confidence interval on the binomial probability.

Usage

binom.confint(x, n, conf.level = 0.95, methods = "all", ...)

Arguments

x

Vector of number of successes in the binomial experiment.

n

Vector of number of independent trials in the binomial experiment.

conf.level

The level of confidence to be used in the confidence interval.

methods

Which method to use to construct the interval. Any combination of c("exact", "ac", "asymptotic", "wilson", "prop.test", "bayes", "logit", "cloglog", "probit") is allowed. Default is "all".

...

Additional arguments to be passed to binom.bayes.

Value

• A data.frame containing the observed proportions and the lower and upper bounds of the confidence interval for all the methods in "methods".

code

(1-alpha/2)^n

Details

Nine methods are allowed for constructing the confidence interval(s):

• exact

{ - Pearson-Klopper method.} asymptotic{ - the text-book definition for confidence limits on a single proportion using the Central Limit Theorem.} agresti-coull{ - Agresti-Coull method.} wilson{ - Wilson method.} prop.test{ - equivalent to prop.test(x = x, n = n, conf.level = conf.level)$conf.int.} bayes{ - see binom.bayes.} logit{ - see binom.logit.} cloglog{ - see binom.cloglog.} probit{ - see binom.probit.} profile{ - see binom.profile.}

References

A. Agresti and B.A. Coull (1998), Approximate is better than "exact" for interval estimation of binomial proportions, American Statistician, 52:119-126.

R.G. Newcombe, Logit confidence intervals and the inverse sinh transformation (2001), American Statistician, 55:200-202.

L.D. Brown, T.T. Cai and A. DasGupta (2001), Interval estimation for a binomial proportion (with discussion), Statistical Science, 16:101-133.

Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (1997) Bayesian Data Analysis, London, U.K.: Chapman and Hall.

See Also

binom.bayes, binom.logit, binom.probit, binom.cloglog, binom.coverage, prop.test

Examples

binom.confint(x = c(2, 4), n = 100, tol = 1e-8)