powerMediation.VSMc {powerMediation} | R Documentation |
Calculate Power for testing mediation effect based on Vittinghoff and McCulloch's (2009) method.
powerMediation.VSMc(n, b2, sigma.m, sigma.e, corr.xm, alpha = 0.05, verbose = TRUE)
n |
sample size. |
b2 |
regression coefficient for the mediator m in the linear regression y_i=b0+b1 x_i + b2 m_i + \epsilon_i, \epsilon_i\sim N(0, \sigma_e^2). |
sigma.m |
standard deviation of the mediator. |
sigma.e |
standard deviation of the random error term in the linear regression y_i=b0+b1 x_i + b2 m_i + \epsilon_i, \epsilon_i\sim N(0, \sigma_e^2). |
corr.xm |
correlation between the predictor x and the mediator m. |
alpha |
type I error rate. |
verbose |
logical. TRUE means printing power; FALSE means not printing power.
|
The power is for testing the null hypothesis b_2=0 versus the alternative hypothesis b_2\neq 0 for the linear regressions:
y_i=b_0+b_1 x_i + b_2 m_i + \epsilon_i, \epsilon_i\sim N(0, \sigma^2_{e})
Vittinghoff et al. (2009) showed that for the above linear regression, testing the mediation effect is equivalent to testing the null hypothesis H_0: b_2=0 versus the alternative hypothesis H_a: b_2\neq 0.
power |
power for testing if b_2=0. |
delta |
b_2\sigma_m\sqrt{1-\rho_{xm}}/\sigma_e, where \sigma_m is the standard deviation of the mediator m, \rho_{xm} is the correlation between the predictor x and the mediator m, and \sigma_e is the standard deviation of the random error term in the linear regression. |
The test is a two-sided test. Code for one-sided tests will be added later.
Weiliang Qiu stwxq@channing.harvard.edu
Vittinghoff, E. and Sen, S. and McCulloch, C.E.. Sample size calculations for evaluating mediation. Statistics In Medicine. 2009;28:541-557.
minEffect.VSMc
,
ssMediation.VSMc
powerMediation.VSMc(n=100, b2=0.8, sigma.m=0.1, sigma.e=0.2, corr.xm=0.5, alpha = 0.05, verbose = TRUE)