Description Usage Arguments Value Author(s) References Examples

Produces 1-alpha confidence intervals for binomial probabilities.

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`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

[email protected]

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.

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harrelfe/Hmisc documentation built on Oct. 3, 2019, 9:34 p.m.

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