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Cleaning up g_mlm_Bayes(), adding to exported functions.
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| rclipboard, | ||
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| brms | ||
| RoxygenNote: 7.2.3 | ||
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| # calculate mlm effect size using Bayesian estimation methods | ||
| #' @title Calculates a between-case standardized mean difference effect size estimate | ||
| #' | ||
| #' @description Estimates a standardized mean difference effect size from a | ||
| #' multi-level model estimated using \code{brms::brm}. | ||
| #' | ||
| #' @param mod Fitted model of class brmsfit (estimated using | ||
| #' \code{brms::brm()}), from which to estimate the effect size. | ||
| #' @param p_const Vector of constants for calculating numerator of effect size. | ||
| #' Must be the same length as fixed effects in \code{mod}. | ||
| #' @param r_const Vector of constants for calculating denominator of effect | ||
| #' size. Must be the same length as the number of variance component | ||
| #' parameters in \code{mod_denom}. | ||
| #' @param rconst_base_var_index Something, not really sure what. | ||
| #' @param cover Confidence level. | ||
| #' | ||
| #' @export | ||
| #' | ||
| #' @return A list with the following components | ||
| #' \tabular{ll}{ | ||
| #' \code{g_AB} \tab Posterior mean effect size estimate \cr | ||
| #' \code{SE_g_AB} \tab Approximate standard error of mean effect size estimate \cr | ||
| #' \code{nu} \tab Estimated denominator degrees of freedom \cr | ||
| #' \code{CI_L} \tab Lower bound of credible interval for effect size \cr | ||
| #' \code{CI_U} \tab Upper bound of credible interval for effect size \cr | ||
| #' \code{es_num_vec} \tab Posterior samples of effect size numerator \cr | ||
| #' \code{es_denom_vec} \tab Posterior samples of squared denominator of effect size \cr | ||
| #' \code{autocor_param} \tab Posterior mean auto-correlation \cr | ||
| #' \code{var_param} \tab Posterior mean of level-1 variance model parameter \cr | ||
| #' \code{rho} \tab Posterior mean intra-class correlation \cr | ||
| #' } | ||
| #' | ||
| #' @references Pustejovsky, J. E., Hedges, L. V., & Shadish, W. R. (2014). | ||
| #' Design-comparable effect sizes in multiple baseline designs: A general | ||
| #' modeling framework. \emph{Journal of Educational and Behavioral Statistics, | ||
| #' 39}(4), 211-227. \doi{10.3102/1076998614547577} | ||
| #' | ||
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| g_mlm_Bayes <- function(mod, p_const, r_const, rconst_base_var_index = 1, cover = 95) { | ||
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| param_names <- brms::variables(mod) | ||
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| # calculate the numerator of BCSMD | ||
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| posterior_samples_fixed <- brms::as_draws_matrix(mod, variable = "^b_", regex = TRUE) | ||
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| if ("b_sigma_Intercept" %in% param_names) { | ||
| samples_fixed <- posterior_samples_fixed[,!startsWith(colnames(posterior_samples_fixed), "b_sigma_")] | ||
| sigma_sq <- exp(2*(posterior_samples_fixed[,"b_sigma_Intercept"])) | ||
| } else { | ||
| samples_fixed <- posterior_samples_fixed | ||
| sigma <- brms::as_draws_matrix(mod, variable = "sigma", regex = TRUE) | ||
| sigma_sq <- sigma^2 | ||
| } | ||
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| es_num_vec <- apply(samples_fixed, 1, function(x) sum(x * p_const)) | ||
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| # calculate the denominator of BCSMD | ||
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| samples_r_sd <- brms::as_draws_matrix(mod, variable = "^sd_", regex = TRUE) | ||
| samples_r_var <- samples_r_sd^2 | ||
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| if (sum(grepl("^cor_",param_names)) > 0) { | ||
| samples_r_cor <- brms::as_draws_matrix(mod, variable = "^cor_", regex = TRUE) | ||
| cor_names_split <- strsplit(colnames(samples_r_cor), split = "__") | ||
| cor_sd_suf <- lapply(cor_names_split, function(x) x[-1]) | ||
| cor_sd_pre <- lapply(cor_names_split, function(x) paste0("sd_", gsub(".*\\_", "", x[[1]]))) | ||
| cor_sd_names <- mapply(function(x,y) paste0(x, "__", y), cor_sd_pre, cor_sd_suf, SIMPLIFY = FALSE) | ||
| sd_prod <- sapply(cor_sd_names, function(x) samples_r_sd[,x[1]] * samples_r_sd[,x[2]]) | ||
| samples_r_cov <- samples_r_cor * sd_prod | ||
| } else { | ||
| samples_r_cov <- NULL | ||
| } | ||
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| samples_r_varcov <- cbind(samples_r_var, samples_r_cov, sigma_sq) | ||
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| es_denom_sq <- apply(samples_r_varcov, 1, function(x) sum(x * r_const)) | ||
| es_denom_vec <- sqrt(es_denom_sq) | ||
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| # calculate BCSMD | ||
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| es_vec <- es_num_vec / es_denom_vec | ||
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| # calculate rho | ||
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| if (rconst_base_var_index == 1) { | ||
| rho <- mean(samples_r_varcov[,1] / (samples_r_varcov[,1] + samples_r_varcov[,ncol(samples_r_varcov)])) | ||
| } else { | ||
| rho_level2 <- mean((samples_r_varcov[,1] + samples_r_varcov[,rconst_base_var_index]) / | ||
| (samples_r_varcov[,1] + samples_r_varcov[,rconst_base_var_index] + | ||
| samples_r_varcov[,ncol(samples_r_varcov)])) | ||
| rho_level2 <- round(rho_level2, 4) | ||
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| rho_level3 <- mean(samples_r_varcov[,1] / | ||
| (samples_r_varcov[,1] + samples_r_varcov[,rconst_base_var_index] + | ||
| samples_r_varcov[,ncol(samples_r_varcov)])) | ||
| rho_level3 <- round(rho_level3, 4) | ||
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| rho <- paste0("Level2: ", rho_level2, " Level3: ", rho_level3) | ||
| } | ||
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| # get the corStruct and varStruct param | ||
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| if (sum(grepl("^ar",param_names)) > 0) { | ||
| autocor_draw <- brms::as_draws_matrix(mod, variable = "^ar", regex = TRUE) | ||
| autocor_param <- mean(autocor_draw) | ||
| } else if (sum(grepl("^ma",param_names)) > 0) { | ||
| autocor_draw <- brms::as_draws_matrix(mod, variable = "^ma", regex = TRUE) | ||
| autocor_param <- mean(autocor_draw) | ||
| } else { | ||
| autocor_param <- NA_real_ | ||
| } | ||
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| if (sum(grepl("^b_sigma_",param_names)) > 0) { | ||
| var_param_name <- setdiff(param_names[startsWith(param_names, "b_sigma_")], "b_sigma_Intercept") | ||
| var_param_draw <- brms::as_draws_matrix(mod, variable = var_param_name, regex = TRUE) | ||
| var_param <- exp(mean(var_param_draw)) | ||
| } else { | ||
| var_param <- NA_real_ | ||
| } | ||
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| g <- mean(es_vec) | ||
| SE_g <- sd(es_vec) | ||
| nu <- 2 * (mean(es_denom_vec))^2 / var(es_denom_vec) | ||
| CI_L <- quantile(es_vec, (1 - cover / 100) / 2) | ||
| CI_U <- quantile(es_vec, (1 + cover / 100) / 2) | ||
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| res <- list( | ||
| g_AB = g, SE_g_AB = SE_g, | ||
| CI_L = CI_L, CI_U = CI_U, | ||
| nu = nu, | ||
| es_num_vec = es_num_vec, | ||
| es_denom_vec = es_denom_vec, | ||
| phi = autocor_param, | ||
| var_param = var_param, | ||
| rho = rho, | ||
| CI_cover = cover, | ||
| converged = NA | ||
| ) | ||
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| class(res) <- c("Bayes-bcsmd", "bcsmd") | ||
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| return(res) | ||
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| } | ||
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