Summary for a cumhist object
Usage
# S3 method for cumhist
summary(object, ...)Arguments
- object
A cumhist object
- ...
Unused
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: 4.321 seconds (Warm-up)
#> Chain 1: 4.917 seconds (Sampling)
#> Chain 1: 9.238 seconds (Total)
#> Chain 1:
summary(br_fit)
#> Call: fit_cumhist(data = br_singleblock, state = "State", duration = "Duration")
#>
#> Family: gamma
#>
#> History parameters:
#> tau = 0.99
#> mixed state = 0.5
#>
#> Linear model:
#> # A tibble: 2 x 5
#> DistributionParameter Term Estimate `5.5%` `94.5%`
#> <fct> <chr> <dbl> <dbl> <dbl>
#> 1 shape History 1.02 0.0693 1.93
#> 2 scale History 0.296 -0.702 1.35
# }