pwr.mdn: Power and Sample Size for Mediation Analysis
In iMediate: Likelihood Methods for Mediation Analysis

Description Usage Arguments Details Value Author(s) References Examples

View source: R/pwr_mdn.R

pwr.mdn Compute power of tests related to mediation analysis or sample size to achieve desired power.

1	pwr.mdn(a, b, c.p, tau1, tau2, n = NULL, power = NULL, alpha = 0.05)

`a`	specified value for coefficient a
`b`	specified value for coefficient b
`c.p`	specified value for coefficient c'
`tau1`	specified value of the ratio of residual variance of mediator M to the variance of the treatment X
`tau2`	specified value of the ratio of residual variance of outcome Y to the variance of the treatment X
`n`	the sample size available. Either `"n"` or `"power"` must be provided
`power`	a value specifying the desired power. Either `"n"` or `"power"` must be provided
`alpha`	specified significance level

This model is for the basic three-factor model. If coefficients are standardized, then τ_1=1-a^2 and τ_2=1-(c')^2-b^2-2abc'.

A 2\times 5 matrix

Kai Wang <kai-wang@uiowa.edu>

Wang, K. (2018) Understanding power anomalies in mediation analysis. Psychometrika 83 (2), 387-406.

n = 100
X = rnorm(n)
s2X = mean((X-mean(X))^2)
a=0.3
b=0.3
c.p = a*b

pwr.mdn(a, b, c.p, 1/s2X, 1/s2X, alpha=0.05, power=0.8)   
pwr.mdn(a, b, c.p, 1/s2X, 1/s2X, alpha=0.05, n=200)

## Using standardized coefficients
pwr.mdn(a, b, c.p, 1-a^2, 1-c.p^2-b^2-2*a*b*c.p, alpha=0.05, power=0.8)
pwr.mdn(a, b, c.p, 1-a^2, 1-c.p^2-b^2-2*a*b*c.p, alpha=0.05, n=200)