The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Posterior Exploration and Publication Graphics

gp3bayes separates numerical posterior summaries from graphics. The same posterior draw matrix can therefore be inspected, tabulated, and plotted without changing the fitted model or its contract.

Backend-independent posterior tables

library(gp3bayes)

draws <- cbind(
  intercept = seq(-1, 1, length.out = 500),
  condition = seq(-0.5, 0.7, length.out = 500)
)

posterior_interval_table(draws)
#>    variable          mean        sd lower        median upper
#> 1 intercept -8.104628e-17 0.5790855 -0.95 -1.110223e-16  0.95
#> 2 condition  1.000000e-01 0.3474513 -0.47  1.000000e-01  0.67
posterior_probability_table(draws, rope = c(-0.1, 0.1))
#>            variable probability_gt_zero probability_lt_zero probability_in_rope
#> intercept intercept               0.500               0.500               0.100
#> condition condition               0.584               0.416               0.166
#>           rope_lower rope_upper
#> intercept       -0.1        0.1
#> condition       -0.1        0.1
posterior_correlation_table(draws)
#>   variable_1 variable_2 correlation  method
#> 1  condition  intercept           1 pearson

Publication graphics

plot_posterior_intervals(draws)

plot_posterior_areas(draws)

The plotting functions return ordinary plotting objects. They do not alter posterior draws, set decision thresholds, or turn interval exclusion into an automatic substantive conclusion.

Fitted-model extraction

For an approved fitted model, the post-fit API standardises extraction through the posterior package:

draw_array <- extract_posterior_draws(fit, regex = "^b_", format = "array")
draw_df <- extract_posterior_draws(fit, regex = "^b_", format = "df")

mcmc_diagnostic_table(fit)
sampler_diagnostic_table(fit)
quality <- summarise_mcmc_quality(fit)

plot_rank_diagnostics(fit)
plot_autocorrelation(fit)
plot_mcmc_quality(quality)
plot_sampler_diagnostics(fit)

Diagnostic flags request inspection. Their absence is not encoded as proof of model adequacy.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.