- main parameters: a, v_perc_acc, t, z, v_Intercept - data within each study are treated as having come from single participant - plot last figure here [Slab + interval stats and geoms • ggdist](https://mjskay.github.io/ggdist/articles/slabinterval.html) ![[hddm_aggregate.png|1400]] # boundary ```r b = 0.04, 95% HPD [−0.45, 0.52] m1 <- brm(a | mi(a_sd) ~ condition + (1 + condition | Study), data = dt3, iter = 10000) # effect of condition b = 0.05 [-0.26, 0.33] > m1 Family: gaussian Links: mu = identity; sigma = identity Formula: a | mi(a_sd) ~ condition + (1 + condition | Study) Data: dt3 (Number of observations: 12) Draws: 4 chains, each with iter = 10000; warmup = 5000; thin = 1; total post-warmup draws = 20000 Group-Level Effects: ~Study (Number of levels: 6) Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS sd(Intercept) 0.59 0.30 0.25 1.37 1.03 96 360 sd(condition) 0.18 0.16 0.01 0.59 1.08 3122 4294 cor(Intercept,condition) -0.33 0.52 -0.96 0.87 1.02 208 6542 Population-Level Effects: Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS Intercept 5.02 0.28 4.49 5.62 1.02 147 2836 condition 0.05 0.14 -0.26 0.33 1.09 4375 3707 Family Specific Parameters: Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS sigma 0.14 0.12 0.02 0.47 1.15 17 14 Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS and Tail_ESS are effective sample size measures, and Rhat is the potential scale reduction factor on split chains (at convergence, Rhat = 1). ``` # weight on accuracy ```r m2 <- brm(b1 | mi(b1_sd) ~ condition + (1 + condition | Study), data = dt3, iter = 10000) # effect of condition b = 0.14 [0.01, 0.29] > m2 Family: gaussian Links: mu = identity; sigma = identity Formula: b1 | mi(b1_sd) ~ condition + (1 + condition | Study) Data: dt3 (Number of observations: 12) Draws: 4 chains, each with iter = 10000; warmup = 5000; thin = 1; total post-warmup draws = 20000 Group-Level Effects: ~Study (Number of levels: 6) Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS sd(Intercept) 0.21 0.14 0.03 0.51 1.17 16 1433 sd(condition) 0.08 0.07 0.00 0.26 1.02 596 6807 cor(Intercept,condition) -0.27 0.56 -0.97 0.90 1.05 52 319 Population-Level Effects: Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS Intercept 0.20 0.11 0.01 0.42 1.14 19 92 condition 0.14 0.07 0.01 0.29 1.05 1876 5090 Family Specific Parameters: Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS sigma 0.07 0.05 0.02 0.20 1.12 21 8 Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS and Tail_ESS are effective sample size measures, and Rhat is the potential scale reduction factor on split chains (at convergence, Rhat = 1). ``` ![[hddm_aggregate_brms_posterior.png|1200]]