Now we extract quantities of interest for more processing/visualization. Samples were drawn using NUTS(diag_e) at Wed Nov 25 17:12:28 2020.ДЇor each parameter, n_eff is a crude measure of effective sample size,ДЄnd Rhat is the potential scale reduction factor on split chains (at Post-warmup draws per chain=250, total post-warmup draws=1000. Y_rep = student_t_rng(nu, mu], sigma]) Д¬LES = normal_cdf(mu_diff / sqrt(sum(sigma)), 0, 1) Д¬LES2 = student_t_rng(nu, mu, sigma) - student_t_rng(nu, mu, sigma) > 0 Real CLES2 // a more explicit approach the mean should roughly equal CLES For brmsfit or stanreg models, Bayes factors are computed using the bridgesampling package. Real cohens_d // effect size see footnote 1 in Kruschke paper If the passed models are supported by insight the DV of all models will be tested for equality (else this is assumed to be true), and the models terms will be extracted (allowing for follow-up analysis with bayesfactorinclusion). Vector y_rep // posterior predictive distribution compare to normal remove all nu specifications if you do this might consider upper bound, as if too large then might as well switch to normal and that the sd here is more informative than in Kruschke paper note that there is a faster implementation of this for stan, Vector sigma // Kruschke puts upper bound as well ignored here Vector mu // estimated group means and sd EM for Latent Variable Approach to Probit.Prior, likelihood, & posterior distributions.Function to find the inverse of a matrix.

Reproducing Kernel Hilbert Space Regression.

Visualize the Posterior Predictive Distribution.Generate the posterior predictive distribution.Alternative specific and constant variables.

Bayes test models