simulate_MB4 {scdhlm} | R Documentation |
Simulate Model MB4 from Pustejovsky, Hedges, & Shadish (2014)
Description
Simulates data from a linear mixed effects model, then calculates REML effect size estimator as described in Pustejovsky, Hedges, & Shadish (2014).
Usage
simulate_MB4(
iterations,
beta,
rho,
phi,
tau2_ratio,
tau_corr,
p_const,
r_const,
design,
m,
n,
MB = TRUE
)
Arguments
iterations |
number of independent iterations of the simulation |
beta |
vector of fixed effect parameters |
rho |
intra-class correlation parameter |
phi |
autocorrelation parameter |
tau2_ratio |
ratio of trend variance to intercept variance |
tau_corr |
correlation between case-specific trends and intercepts |
p_const |
vector of constants for calculating numerator of effect size |
r_const |
vector of constants for calculating denominator of effect size |
design |
design matrix. If not specified, it will be calculated based on |
m |
number of cases. Not used if |
n |
number of measurement occasions. Not used if |
MB |
If true, a multiple baseline design will be used; otherwise, an AB design will be used. Not used if |
Value
A matrix reporting the mean and variance of the effect size estimates and various associated statistics.
References
Pustejovsky, J. E., Hedges, L. V., & Shadish, W. R. (2014). Design-comparable effect sizes in multiple baseline designs: A general modeling framework. Journal of Educational and Behavioral Statistics, 39(4), 211-227. doi:10.3102/1076998614547577
Examples
simulate_MB4(iterations = 5, beta = c(0,1,0,0), rho = 0.8, phi = 0.5,
tau2_ratio = 0.5, tau_corr = 0,
p_const = c(0,1,0,7), r_const = c(1,0,1,0,0),
design = design_matrix(3, 16, treat_times=c(5,9,13), center = 12))
simulate_MB4(iterations = 5, beta = c(0,1,0,0), rho = 0.8, phi = 0.5,
tau2_ratio = 0.5, tau_corr = 0, m = 6, n = 8)