Power analysis for t-test based on Monte Carlo simulation
Power analysis for t-test based on Monte Carlo simulation
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"))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 |
Zhang, Z., & Yuan, K.-H. (2018). Practical Statistical Power Analysis Using Webpower and R (Eds). Granger, IN: ISDSA Press.
########## 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"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.