diagnostics {dreamer} | R Documentation |
Calculate MCMC Diagnostics for Parameters
Description
Calculate MCMC diagnostics for individual parameters.
Usage
diagnostics(x)
Arguments
x |
MCMC output from a dreamer model. |
Value
A tibble listing the Gelman point estimates and upper bounds (obtained from coda::gelman.diag) and effective sample size (obtained from coda::effectiveSize) for each parameter within each model.
Examples
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
)
)
# for all models
diagnostics(output)
# for a single model
diagnostics(output$mod_quad)
[Package dreamer version 3.1.0 Index]