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

## Description

`utest.ppow` computes (via simulation) the power of an experiment that will be analyzed using a u test (also called Wilcoxon test). Rather than taking a theoretical distribution, this function takes empirical data and bootstraps them to calculate the power.

## Usage

 ```1 2``` ```utest.ppow(x, y = NULL, n, r = 10000, alternative = c("two.sided", "less", "greater"), mu = NULL, alpha = 0.05, conf.level = 0.95) ```

## Arguments

 `x` a data frame with two columns, or a list with pilot data. `y` a list with pilot data. When x is a list and y is not provided, a one-tailed t-test is used.

## Value

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

## Examples

 ```1 2 3``` ```utest.ppow(x=c(0, 5, 10), n=16) # Power for a one-sample u test with n=16. Pilot data consists of three data points. utest.ppow(x=c(0, 5, 10), n=16, mu = -5) # Same as above, changing the avarege under the null to -5. utest.ppow(x=c(0, 5, 10), y=c(9, 3, 2, 1), n=30) # Power for a two-sample u test with n=30 (per condition) using unbalanced pilot data. ```

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