# binconf

##### Confidence Intervals for Binomial Probabilities

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")
# }
```

*Documentation reproduced from package Hmisc, version 4.3-1, License: GPL (>= 2)*