simulate_MB2: Simulate Model MB2 from Pustejovsky, Hedges, & Shadish (2014) In scdhlm: Estimating Hierarchical Linear Models for Single-Case Designs

Description

Simulates data from a linear mixed effects model, then calculates REML effect size estimator as described in Pustejovsky, Hedges, & Shadish (2014).

Usage

 1 2 3 4 5 6 7 8 9 10 11 12 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

 1 2 3 4 5 6 7 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, 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)

scdhlm documentation built on Jan. 13, 2021, 7:10 p.m.