Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/binom.confint.R

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

1 | ```
binom.confint(x, n, conf.level = 0.95, methods = "all", ...)
``` |

`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 |

`...` |
Additional arguments to be passed to |

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

`exact`

- Pearson-Klopper method. See also`binom.test`

.`asymptotic`

- the text-book definition for confidence limits on a single proportion using the Central Limit Theorem.`agresti-coull`

- Agresti-Coull method. For a 95% confidence interval, this method does not use the concept of "adding 2 successes and 2 failures," but rather uses the formulas explicitly described in the following link: http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Agresti-Coull_Interval.`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`

.

By default all eight are estimated for each value of `x`

and/or
`n`

. For the "logit", "cloglog", "probit", and "profile"
methods, the cases where `x == 0`

or `x == n`

are treated
separately. Specifically, the lower bound is replaced by
`(alpha/2)^n`

and the upper bound is replaced by `(1-alpha/2)^n`

.

A `data.frame`

containing the observed proportions and
the lower and upper bounds of the confidence interval for all the
methods in `"methods"`

.

Sundar Dorai-Raj ([email protected])

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.

`binom.bayes`

, `binom.logit`

,
`binom.probit`

, `binom.cloglog`

,
`binom.coverage`

, `prop.test`

,
`binom.test`

for comparison to method
`"exact"`

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

binom documentation built on May 29, 2017, 2:26 p.m.

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