Calculates a between-case standardized mean difference effect size estimate

Estimates a standardized mean difference effect size from a multi-level model estimated using brms::brm.

g_mlm_Bayes(mod, p_const, r_const, rconst_base_var_index = 1, cover = 95)

Arguments

mod: Fitted model of class brmsfit (estimated using brms::brm()), from which to estimate the effect size.
p_const: Vector of constants for calculating numerator of effect size. Must be the same length as fixed effects in mod.
r_const: Vector of constants for calculating denominator of effect size. Must be the same length as the number of variance component parameters in mod_denom.
rconst_base_var_index: Something, not really sure what.
cover: Confidence level.

Value

A list with the following components

`g_AB`	Posterior mean effect size estimate
`SE_g_AB`	Approximate standard error of mean effect size estimate
`nu`	Estimated denominator degrees of freedom
`CI_L`	Lower bound of credible interval for effect size
`CI_U`	Upper bound of credible interval for effect size
`es_num_vec`	Posterior samples of effect size numerator
`es_denom_vec`	Posterior samples of squared denominator of effect size
`autocor_param`	Posterior mean auto-correlation
`var_param`	Posterior mean of level-1 variance model parameter
`rho`	Posterior mean intra-class correlation

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