scdhlm (version 0.7.3)

simulate_MB2: 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
)

Value

A matrix reporting the mean and variance of the effect size estimates and various associated statistics.

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.

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. tools:::Rd_expr_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)