# binom.pexplore: Explore the power of a binomial experiment under different... In julianje/mcpa: Intuitive power analyses through monte carlo simulations

## Description

`binom.pexplore` computes (through simulation) the power of a binomial experiment under different sample sizes. Rather than taking a probability of success (like `binom.explore`), `binom.pexplore` takes a vector of pilot data.

## Usage

 ```1 2 3``` ```binom.pexplore(lown, topn, pilotdata, r = 10000, alternative = c("two.sided", "less", "greater"), alpha = 0.05, nullp = 0.5, conf.level = 0.95, plotit = TRUE) ```

## Arguments

 `lown` smallest sample size to explore. `topn` largest sample size to explore. `r` number of simulations to compute power. `alternative` type of alternative hypothesis in binomial test. Must be "`two.sided`" (default), "`greater`", or "`less`". `alpha` significance threshhold. `nullp` probability of success in null hypothesis. `conf.level` size of confidence intervals. `plotit` logical (default=`TRUE`) value. Function generates a plot when `TRUE` and returns a data frame otherwise.

## Value

The probability of finding p < α with the experiment description and a 95

## See Also

`binom.power`, `binom.ppow`, `binom.explore`, and `binom.pexplore`.

## Examples

 ```1 2 3``` ```binom.pexplore(lown=16, topn=24, pilotdata = (1, 1, 0, 0, 1, 1), p=0.8) binom.pexplore(lown=16, topn=24, pilotdata = ("a", "b", "b", "b"), p=0.8, alternative="greater") binom.pexplore(lown=16, topn=24, pilotdata = ("a", "b", "b", "b"), r=5000, nullp=0.25, alternative="greater") ```

julianje/mcpa documentation built on May 13, 2019, 6:14 p.m.