# wp.mc.t: Power analysis for t-test based on Monte Carlo simulation In WebPower: Basic and Advanced Statistical Power Analysis

## Description

Power analysis for t-test based on Monte Carlo simulation

## Usage

 ```1 2 3 4``` ```wp.mc.t(n = NULL, R0 = 1e+05, R1 = 1000, mu0 = 0, mu1 = 0, sd = 1, skewness = 0, kurtosis = 3, alpha = 0.05, type = c("two.sample", "one.sample", "paired"), alternative = c("two.sided", "less", "greater")) ```

## Arguments

 `n` Sample size `R0` Number of replications under the null `R1` Number of replications `mu0` Population mean under the null `mu1` Population mean under the alternative `sd` Standard deviation `skewness` Skewness `kurtosis` kurtosis `alpha` Significance level `type` Type of anlaysis `alternative` alternative hypothesis

## References

Zhang, Z., & Yuan, K.-H. (2018). Practical Statistical Power Analysis Using Webpower and R (Eds). Granger, IN: ISDSA Press.

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```########## Chapter 16. Monte Carlo t-test ############# wp.mc.t(n=20 , mu0=0, mu1=0.5, sd=1, skewness=0, kurtosis=3, type = c("one.sample"), alternative = c("two.sided")) wp.mc.t(n=40 , mu0=0, mu1=0.3, sd=1, skewness=1, kurtosis=6, type = c("paired"), alternative = c("greater")) wp.mc.t(n=c(15, 15), mu1=c(0.2, 0.5), sd=c(0.2, 0.5), skewness=c(1, 2), kurtosis=c(4, 6), type = c("two.sample"), alternative = c("less")) ```

WebPower documentation built on May 1, 2019, 8:19 p.m.