simulate_MB2: Simulate Model MB2 from Pustejovsky, Hedges, & Shadish (2014)

In jepusto/scdhlm: Estimating Hierarchical Linear Models for Single-Case Designs

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. \Sexpr[results=rd]{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)
             

jepusto/scdhlm documentation built on Feb. 27, 2024, 4:45 p.m.