![[Experimental]](figures/lifecycle-experimental.svg)
Usage
prior_summary(object, ...)
# S3 method for class 'mcpfit'
prior_summary(object, verbose = FALSE, ...)Value
A tibble with one row per model parameter, ordered and labeled
the same way as summary(): change points first, then mu, then the
other distributional parameters, then AR/MA lags - each with
segment and dpar columns.
Details
Shows the specified priors in the syntax of mcp(..., prior), resolved to numbers.
As in prior, scales are SD/scale rather than JAGS precision (see mcp-priors). bounds are the lower and upper bounds of
each prior, i.e., the arguments of dunif() or T(), or the support of the distribution.
Change points are also "ordered": their specified priors are restricted to ordered change
points. Use plot_pars(fit, prior = TRUE) to see the marginal priors after this restriction.
verbose = TRUE also displays the underlying rule, description, source, and kind.
Numbers are rounded to 5 significant digits for display. fit$prior holds the exact
values used in the model. Parameter names within a prior describe its dependence on
other parameters, e.g., the default prior on Intercept_1 given x_1.
prior_summary() shares its generic with rstantools (used by brms and rstanarm), so it
works for all of these models regardless of package load order.
See also
mcp-priors for how to specify priors, and Default priors for the rules behind them.
Examples
prior_summary(demo_fit) # Show the resolved priors and their bounds
#> # A tibble: 7 × 5
#> parameter segment dpar prior bounds
#> <chr> <int> <chr> <chr> <chr>
#> 1 cp_1 2 NA dunif(0.61863, 99.741) [0.61863, 99.741], ordered
#> 2 cp_2 3 NA dunif(0.61863, 99.741) [0.61863, 99.741], ordered
#> 3 Intercept_1 1 mu dnorm(13.825, 17.383) none
#> 4 time_2 2 mu dnorm(0, 0.62124) none
#> 5 Intercept_3 3 mu dnorm(13.825, 17.383) none
#> 6 time_3 3 mu dnorm(0, 0.62124) none
#> 7 sigma_1 1 sigma dt(0, 5.7, 3) T(0, ) [0, Inf]
prior_summary(demo_fit, verbose = TRUE) # Include their rules and sources
#> # A tibble: 7 × 9
#> parameter segment dpar prior bounds rule description source kind
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 cp_1 2 NA dunif(0.61863, 99.741) [0.61863, 99.741], ordered dunif(min(time), max(time)) Flat Dirichlet on the segment lengths default distribution
#> 2 cp_2 3 NA dunif(0.61863, 99.741) [0.61863, 99.741], ordered dunif(min(time), max(time)) Flat Dirichlet on the segment lengths default distribution
#> 3 Intercept_1 1 mu dnorm(13.825, 17.383) none dnorm(mean(response), 2.5 * sd(response)) Mean intercept (rstanarm default) default distribution
#> 4 time_2 2 mu dnorm(0, 0.62124) none dnorm(0, 2.5 * sd(response)/(sd(time))) Autoscaled mean coefficient (rstanarm default) default distribution
#> 5 Intercept_3 3 mu dnorm(13.825, 17.383) none dnorm(mean(response), 2.5 * sd(response)) Mean intercept (rstanarm default) default distribution
#> 6 time_3 3 mu dnorm(0, 0.62124) none dnorm(0, 2.5 * sd(response)/(sd(time))) Autoscaled mean coefficient (rstanarm default) default distribution
#> 7 sigma_1 1 sigma dt(0, 5.7, 3) T(0, ) [0, Inf] dt(0, max(2.5, round(mad(response), 1)), 3) T(0, ) Positive residual SD calibrated on the response scale default distribution