# binom.power: Power curves for binomial parameterizations In binom: Binomial Confidence Intervals For Several Parameterizations

## Description

Uses Wald statistics to compute power curves for several parameterizations.

## Usage

 ```1 2 3 4 5 6 7``` ```binom.power(p.alt, n = 100, p = 0.5, alpha = 0.05, phi = 1, alternative = c("two.sided", "greater", "less"), method = c("cloglog", "logit", "probit", "asymp", "lrt", "exact")) ```

## Arguments

 `p.alt` A vector of success probabilities under the alternative hypothesis. `n` A vector representing the number of independent trials in the binomial experiment. `p` A vector of success probabilities under the null hypothesis. `alpha` A vector of type-I error rates. `phi` A vector determining the overdispersion parameter for each binomial experiment. `alternative` Type of alternative hypothesis. `method` The method used to compute power.

## Details

For derivations see doc/binom.pdf. `p.alt`, `n`, `p`, `alpha`, and `phi` can all be vectors. The length of each argument will be expanded to the longest length. The function assumes the lengths are equal or can be wrapped for multiple values.

## Value

The estimated probability of detecting the difference between `p.alt` and `p`.

## Author(s)

Sundar Dorai-Raj ([email protected])

`binom.confint`, `binom.bayes`, `binom.logit`, `binom.probit`, `binom.coverage`

## Examples

 `1` ```binom.power(0.95, alternative = "greater") ```

### Example output

```    alpha
0.9999999
```

binom documentation built on May 31, 2017, 2:59 a.m.