simulate_MB2 {scdhlm}

Simulate Model MB2 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_MB2(
  iterations,
  beta,
  rho,
  phi,
  tau1_ratio,
  tau_corr,
  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

tau1_ratio

ratio of treatment effect variance to intercept variance

tau_corr

correlation between case-specific treatment effects and intercepts

design

design matrix. If not specified, it will be calculated based on m, n, and MB.

m

number of cases. Not used if design is specified.

n

number of measurement occasions. Not used if design is specified.

MB

If true, a multiple baseline design will be used; otherwise, an AB design will be used. Not used if design is specified.

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

set.seed(8)
simulate_MB2(iterations = 5, beta = c(0,1,0,0), rho = 0.4, phi = 0.5, 
             tau1_ratio = 0.5, tau_corr = -0.4, design = design_matrix(m=3, n=8))
             
set.seed(8)
simulate_MB2(iterations = 5, beta = c(0,1,0,0), rho = 0.4, phi = 0.5, 
             tau1_ratio = 0.5, tau_corr = -0.4, m = 3, n = 8, MB = FALSE)