utest.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 u test (also called Wilcoxon 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.

Usage

 ```1 2 3``` ```utest.pexplore(x, y = NULL, lown, topn, r = 10000, alternative = c("two.sided", "less", "greater"), mu = NULL, alpha = 0.05, plotit = TRUE, conf.level = 0.95) ```

Arguments

 `lown` smallest sample size to explore. `topn` largest sample size to explore. `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.

Examples

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

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