# fishers.pow: Compute the power using a one- or two-sample unpaired t-test. In julianje/mcpa: Intuitive power analyses through monte carlo simulations

## Description

`ttest.pow` computes (via simulation) the power of an experiment that will be analyzed using a t-test. When two means are provided, function assumes a two-sample unpaired t-test, and `n` is interpreted as the sample size of each group (for a total sample size or `2n`).

## Usage

 ```1 2``` ```fishers.pow(means, var, n, r = 10000, alternative = c("two.sided", "less", "greater"), mu = NULL, alpha = 0.05) ```

## Arguments

 `means` either a list with two average values (computes a two-sample t-test) or a single value (computes a one-sample t-test). `var` expected variance in each group. `n` sample size. `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.

## Value

The probability of finding p < α with the experiment description.

`ttest.pow`, `ttest.ppow`, `ttest.explore`, and `ttest.pexplore`.
 ```1 2 3``` ```ttest.pow(means=c(5, 10), var=10, n=16) # two-sample t-test. n=16 refers to each condition, for a total of 32. ttest.pow(means=20, var=10, n=16) # one-sample t-test. Comparing if average is different from 0. Because there is only condition, the total sample isze is 16. ttest.pow(means=20, var=10, n=16, mu=10, alternative="higher") # one-sample t-test. Comparing if average is higher than 10. ```