simulate_MB2: Simulate Model MB2 from Pustejovsky, Hedges, & Shadish (2014) In jepusto/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``` ```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``` ```set.seed(8) simulate_MB2(iterations = 10, 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 = 10, 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 Aug. 23, 2018, 6:54 a.m.