wp.modmed.m58 | R Documentation |
power analysis of model 58 in Introduction to Mediation, Moderation, and Conditional Process Analysis
wp.modmed.m58(
c1,
a1,
c2,
d1,
b1,
b2,
cp,
sige12,
sige22,
sigx_w,
n,
sigx2 = 1,
sigw2 = 1,
nrep = 1000,
alpha = 0.05,
b = 1000,
nb = n,
w_value = 0,
power_method = "product",
MCrep = 1000,
ncore = 1,
simulation_method = "percentile",
pop.cov = NULL,
mu = NULL,
varnames = c("x", "w", "m", "xw", "mw", "y")
)
c1 |
regression coefficient of outcome (m) on moderator (w) |
a1 |
regression coefficient of mediator (m) on predictor (x) |
c2 |
regression coefficient of outcome (m) on the product (xw) |
d1 |
regression coefficient of outcome (y) on moderator (w) |
b1 |
regression coefficient of outcome (y) on mediator (m) |
b2 |
regression coefficient of outcome (y) on the product (mw) |
cp |
regression coefficient of outcome (y) on predictor (x) |
sige12 |
variance of error in the first regression equation |
sige22 |
variance of error in the second regression equation |
sigx_w |
covariance between predictor (x) and moderator (w) |
n |
sample size |
sigx2 |
variance of predictor (x) |
sigw2 |
variance of moderator (w) |
nrep |
number of replications for finding power |
alpha |
type 1 error rate |
b |
number of bootstrap iterations used when simulation method is "percentile" |
nb |
bootstrap sample size, default to n, used when simulation method is "percentile" |
w_value |
moderator level |
power_method |
"product" for using the indirect effect value in power calculation, or "joint" for using joint significance in power calculation |
MCrep |
number of repetitions used for finding distribution when simulation method is "MC" |
ncore |
number of cores to use, default is 1, when ncore > 1, parallel is used |
simulation_method |
"percentile" for using percentile bootstrap CI in finding significance of mediation, or "MC" for using Monte Carlo CI in finding significance of mediation |
pop.cov |
covariance matrix, default to NULL if using the regression coefficient approach |
mu |
mean vector, default to NULL if using the regression coefficient approach |
varnames |
name of variables for the covariance matrix |
power of indirect effect, direct effect, and moderation
Xu, Z., Gao, F., Fa, A., Qu, W., & Zhang, Z. (2023). Statistical Power Analysis and Sample Size Planning for Moderated Mediation Models. Zhang, Z., & Yuan, K.-H. (2018). Practical Statistical Power Analysis Using Webpower and R (Eds). Granger, IN: ISDSA Press.
test = wp.modmed.m58(c1 = 0.2, a1 = 0.2, c2 = 0.1, b2 = 0.1,
b1 = 0.2, cp = 0.2, d1 = 0.2, w_value = 0.3, simulation_method = "MC",
sigx2 = 1, sigw2 = 1, sige12 = 1, sige22 = 1, sigx_w = 0.5,
n = 50, nrep = 1000, alpha = 0.05, ncore = 1)
print(test)
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