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Posterior intervals

Source: R/posterior_interval.R
posterior_interval.blrmfit.Rd

Posterior intervals of all model parameters.

Usage

# S3 method for class 'blrmfit'
posterior_interval(object, prob = 0.95, ...)

Arguments

object

fitted model object

prob

central probability mass to report, i.e. the quantiles 0.5-prob/2 and 0.5+prob/2 are displayed. Multiple central widths can be specified.

...

not used in this function

Value

Matrix of two columns for the central probability interval prob for all parameters of the model.

Details

Reports the quantiles of posterior parameters which correspond to the central probability mass specified. The output includes the posterior of the hyper-parameters and the posterior of each group estimate.

Examples

## Setting up dummy sampling for fast execution of example
## Please use 4 chains and 100x more warmup & iter in practice
.user_mc_options <- options(
  OncoBayes2.MC.warmup = 10, OncoBayes2.MC.iter = 20, OncoBayes2.MC.chains = 1,
  OncoBayes2.MC.save_warmup = FALSE
)

example_model("single_agent", silent = TRUE)
#> Warning: The largest R-hat is NA, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess

posterior_interval(blrmfit)
#>                                                      2.5%     97.5%
#> mu_log_beta[log(drug_A/dref),intercept]        -0.8640091 3.8839930
#> mu_log_beta[log(drug_A/dref),log_slope]        -0.4132765 1.5579169
#> tau_log_beta[1,log(drug_A/dref),intercept]      0.0000000 0.0000000
#> tau_log_beta[1,log(drug_A/dref),log_slope]      0.0000000 0.0000000
#> rho_log_beta[log(drug_A/dref)]                 -0.8234919 0.8211436
#> beta_group[trial_A,log(drug_A/dref),intercept] -0.8640091 3.8839930
#> beta_group[trial_A,log(drug_A/dref),slope]      0.7324769 4.7797538

## Recover user set sampling defaults
options(.user_mc_options)