Extracts models population-level coefficients history-specific terms for every modeled distribution parameter.
Usage
historyef(object, summary = TRUE, probs = c(0.055, 0.945))Arguments
- object
An object of class cumhist
- summary
Whether summary statistics should be returned instead of raw sample values. Defaults to
TRUE- probs
The percentiles used to compute summary, defaults to 89% credible interval.
Examples
# \donttest{
br_fit <- fit_cumhist(br_singleblock, state="State", duration="Duration")
#>
#> SAMPLING FOR MODEL 'historylm' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 4.881 seconds (Warm-up)
#> Chain 1: 4.963 seconds (Sampling)
#> Chain 1: 9.844 seconds (Total)
#> Chain 1:
historyef(br_fit)
#> # A tibble: 2 x 4
#> DistributionParameter Estimate `5.5%` `94.5%`
#> <fct> <dbl> <dbl> <dbl>
#> 1 shape 1.05 0.104 2.00
#> 2 scale 0.279 -0.767 1.30
# }