# chisq.pexplore: Explore power as a function of sample sie using a one- or... In julianje/mcpa: Intuitive power analyses through monte carlo simulations

## Description

`ttest.pexplore` computes (via simulation) the power of an experiment that will be analyzed using a t-test for a range of sample sizes. Rather than taking a theoretical distribution, this function takes empirical data and bootstraps them to calculate the power. For an equivalent function that does not rely on pilot data see ttestpower.

## Usage

 ```1 2 3``` ```chisq.pexplore(x, y = NULL, lown, topn, r = 10000, alternative = c("two.sided", "less", "greater"), mu = NULL, alpha = 0.05, 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`". `mu` mean value according to null hypothesis (default = `0`). Only used in one sample t-tests. `alpha` significance threshhold. `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.

`ttest.pow`, `ttest.ppow`, `ttest.explore`, and `ttest.pexplore`.
 ```1 2 3``` ```ttest.pexplore(x=c(0, 5, 10), lown=16, topn=24) # Power for a one-sample t-test with n in 16-24. Pilot data consists of three data points. ttest.pexplore(x=c(0, 5, 10), lown=16, topn=24,mu = -5) # Same as above, changing the avarege under the null to -5. ttest.pexplore(x=c(0, 5, 10), lown=16, topn=24, y=c(9, 3, 2, 1)) # Power for a two-sample t-test with n=16-24 (per condition) using unbalanced pilot data. ```