wp.mc.t: Power analysis for t-test based on Monte Carlo simulation

In WebPower: Basic and Advanced Statistical Power Analysis

Power analysis for t-test based on Monte Carlo simulation

Description

Power analysis for t-test based on Monte Carlo simulation

Usage

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

set.seed(20220722)
########## 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 Oct. 14, 2023, 1:06 a.m.