# power.binom.test: Power Calculations for Exact Binomial Test In jmuOutlier: Permutation Tests for Nonparametric Statistics

## Description

Compute the power of the binomial test of a simple null hypothesis about a population median.

## Usage

 ```1 2``` ```power.binom.test(n, alpha = 0.05, alternative = c("two.sided", "less", "greater"), null.median, alt.pdist, ...) ```

## Arguments

 `n` The sample size. `alpha` Probability of Type I error. `alternative` A character string specifying the alternative hypothesis, and must be one of `"two.sided"` (default), `"greater"` or `"less"`. Only the initial letter needs to be specified. `null.median` The population median under the null hypothesis. `alt.pdist` Name of the cumulative distribution function under the alternative distribution. Some options include ``` pnorm, pexp, pcauchy, plaplace, pt, pchisq, pf, ptriang, punif, pbinom, pgeom, ppois. ``` `...` Optional arguments to `alt.pdist`, excluding the first argument of `alt.pdist`. See the examples below.

## Value

Power of the test.

## Author(s)

Steven T. Garren, James Madison University, Harrisonburg, Virginia, USA

## References

Higgins, J. J. (2004) Introduction to Modern Nonparametric Statistics.

`power.t.test`

## Examples

 ```1 2 3 4 5``` ```# Alternative distribution is Normal( mean=55.7, sd=2.5 ). power.binom.test( 30, 0.05, "greater", 55, pnorm, 55.7, 2.5 ) # Alternative distribution is Laplace( mean=55.7, sd=2.5 ). power.binom.test( 30, 0.05, "greater", 55, plaplace, 55.7, 2.5 ) ```

### Example output

```[1] 0.3321649
[1] 0.570145
```

jmuOutlier documentation built on Aug. 6, 2019, 1:03 a.m.