summary.dreamer_bma | R Documentation |
Summarize parameter inference and convergence diagnostics.
## S3 method for class 'dreamer_bma' summary(object, ...)
object |
a dreamer MCMC object. |
... |
additional arguments (which are ignored). |
Returns a named list with elements model_weights
and summary
containing the prior and posterior weights for each model and inference
on parameters for each model as well as MCMC diagnostics.
set.seed(888) data <- dreamer_data_linear( n_cohorts = c(20, 20, 20), dose = c(0, 3, 10), b1 = 1, b2 = 3, sigma = 5 ) # Bayesian model averaging output <- dreamer_mcmc( data = data, n_adapt = 1e3, n_burn = 1e3, n_iter = 1e4, n_chains = 2, silent = FALSE, mod_linear = model_linear( mu_b1 = 0, sigma_b1 = 1, mu_b2 = 0, sigma_b2 = 1, shape = 1, rate = .001, w_prior = 1 / 2 ), mod_quad = model_quad( mu_b1 = 0, sigma_b1 = 1, mu_b2 = 0, sigma_b2 = 1, mu_b3 = 0, sigma_b3 = 1, shape = 1, rate = .001, w_prior = 1 / 2 ) ) # all models (also show model weights) summary(output) # single model summary(output$mod_linear)
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