Simulate Model MB4 from Pustejovsky, Hedges, & Shadish (2014)

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Description

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

Usage

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simulate_MB4(iterations, beta, rho, phi, tau2_ratio, tau_corr, p_const, r_const,
  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

tau2_ratio

ratio of trend variance to intercept variance

tau_corr

correlation between case-specific trends and intercepts

p_const

vector of constants for calculating numerator of effect size

r_const

vector of constants for calculating denominator of effect size

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

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simulate_MB4(iterations = 10, beta = c(0,1,0,0), rho = 0.8, phi = 0.5, 
             tau2_ratio = 0.5, tau_corr = 0, 
             p_const = c(0,1,0,7), r_const = c(1,0,1,0,0), 
             design = design_matrix(3, 16, treat_times=c(5,9,13), center = 12))
simulate_MB4(iterations = 10, beta = c(0,1,0,0), rho = 0.8, phi = 0.5, 
             tau2_ratio = 0.5, tau_corr = 0, m = 6, n = 8)