# clopper.pearson.ci: Clopper-Pearson Confidence Interval In GenBinomApps: Clopper-Pearson Confidence Interval and Generalized Binomial Distribution

## Description

Computing upper, lower or two-sided Clopper-Pearson confidence limits for a given confidence level.

## Usage

 `1` ```clopper.pearson.ci(k, n, alpha = 0.1, CI = "upper") ```

## Arguments

 `k` number of failures/successes. `n` number of trials. `alpha` significance level for the (1-alpha)* 100% confidence level (default alpha=0.1). `CI` indicates the kind of the confidence interval, options: "upper" (default), "lower", "two.sided".

## Details

Computes the confidence limits for the p of a binomial distribution. Confidence intervals are obtained by the definition of Clopper and Pearson.

## Value

A data frame containing the kind of the confidence interval, upper and lower limits and the used significance level `alpha`.

## References

D.Kurz, H.Lewitschnig, J.Pilz, Decision-Theoretical Model for Failures which are Tackled by Countermeasures, IEEE Transactions on Reliability, Vol. 63, No. 2, June 2014.

C.J.Clopper and E.S. Pearson, The use of confidence or fiducial limits illustrated in the case of the binomial, Biometrika, vol. 26, 404-413, 1934.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```clopper.pearson.ci(5,100000,alpha=0.05) # Confidence.Interval = upper # Lower.limit = 0 # Upper.limit = 0.0001051275 # alpha = 0.05 clopper.pearson.ci(5,100000,CI="two.sided") # Confidence.Interval = two.sided # Lower.limit = 1.97017e-05 # Upper.limit = 0.0001051275 # alpha = 0.1 ```

### Example output

``` Confidence.Interval Lower.limit  Upper.limit alpha
upper           0 0.0001051275  0.05
Confidence.Interval Lower.limit  Upper.limit alpha
two.sided 1.97017e-05 0.0001051275   0.1
```

GenBinomApps documentation built on May 29, 2017, 3:58 p.m.