Summary method for a discharge rating curve tournament
Source:R/tournament_methods.R
summary.tournament.RdPrint the summary of a tournament of model comparisons. This function allows for an efficient and fast re-run of the tournament with different methods or winning criteria.
Usage
# S3 method for class 'tournament'
summary(object, method = NULL, winning_criteria = NULL, ...)Arguments
- object
An object of class "tournament"
- method
Optional; a string specifying the method to use for the summary. If NULL, uses the method from the original tournament. Options are "WAIC", "DIC", or "PMP".
- winning_criteria
Optional; specifies new winning criteria for the summary. If NULL, uses the criteria from the original tournament. See Details in
tournamentfor proper formatting.- ...
Not used in this function
Details
If either method or winning_criteria is provided, the function re-runs the tournament with the new parameters using the fitted models.
See also
tournament to run a discharge rating curve tournament and plot.tournament for visualizing the model comparison
Examples
# \donttest{
data(krokfors)
set.seed(1)
t_obj <- tournament(Q ~ W, krokfors, num_cores = 2)
#> Running tournament [ ] 0%
#>
#> Progress:
#> Initializing Metropolis MCMC algorithm...
#> Multiprocess sampling (4 chains in 2 jobs) ...
#>
#> MCMC sampling completed!
#>
#> Diagnostics:
#> Acceptance rate: 25.33%.
#> ✔ All chains have mixed well (Rhat < 1.1).
#> ✔ Effective sample sizes sufficient (eff_n_samples > 400).
#>
#> ✔ gplm finished [============ ] 25%
#>
#> Progress:
#> Initializing Metropolis MCMC algorithm...
#> Multiprocess sampling (4 chains in 2 jobs) ...
#>
#> MCMC sampling completed!
#>
#> Diagnostics:
#> Acceptance rate: 31.14%.
#> ✔ All chains have mixed well (Rhat < 1.1).
#> ✔ Effective sample sizes sufficient (eff_n_samples > 400).
#>
#> ✔ gplm0 finished [======================== ] 50%
#>
#> Progress:
#> Initializing Metropolis MCMC algorithm...
#> Multiprocess sampling (4 chains in 2 jobs) ...
#>
#> MCMC sampling completed!
#>
#> Diagnostics:
#> Acceptance rate: 25.66%.
#> ✔ All chains have mixed well (Rhat < 1.1).
#> ✔ Effective sample sizes sufficient (eff_n_samples > 400).
#>
#> ✔ plm finished [==================================== ] 75%
#>
#> Progress:
#> Initializing Metropolis MCMC algorithm...
#> Multiprocess sampling (4 chains in 2 jobs) ...
#>
#> MCMC sampling completed!
#>
#> Diagnostics:
#> Acceptance rate: 36.04%.
#> ✔ All chains have mixed well (Rhat < 1.1).
#> ✔ Effective sample sizes sufficient (eff_n_samples > 400).
#>
#> ✔ plm0 finished [================================================] 100%
summary(t_obj)
#>
#> === Tournament Model Comparison Summary ===
#>
#> Method: WAIC
#> Winning Criteria: Delta_WAIC > 2
#> Overall Winner: gplm0
#>
#> Comparison 1 Results:
#> --------------------------------------------------------------------------------------------------
#> complexity model winner lppd eff_num_param WAIC SE_WAIC Delta_WAIC SE_Delta_WAIC
#> more gplm 20.7794 6.8706 -27.8176 11.8918 0.5570 0.2416
#> less gplm0 <--- 20.3710 6.7406 -27.2606 12.0360
#>
#> Comparison 2 Results:
#> --------------------------------------------------------------------------------------------------
#> complexity model winner lppd eff_num_param WAIC SE_WAIC Delta_WAIC SE_Delta_WAIC
#> more plm 5.5842 4.2574 -2.6536 6.6635 -0.4066 0.1904
#> less plm0 <--- 5.6284 4.0984 -3.0601 6.6931
#>
#> Comparison 3 Results:
#> --------------------------------------------------------------------------------------------------
#> complexity model winner lppd eff_num_param WAIC SE_WAIC Delta_WAIC SE_Delta_WAIC
#> more gplm0 <--- 20.3710 6.7406 -27.2606 12.0360 24.2005 9.1834
#> less plm0 5.6284 4.0984 -3.0601 6.6931
#>
#> === End of Summary ===
# Re-run summary with different method
summary(t_obj, method = "DIC")
#>
#> === Tournament Model Comparison Summary ===
#>
#> Method: DIC
#> Winning Criteria: Delta_DIC > 2
#> Overall Winner: gplm0
#>
#> Comparison 1 Results:
#> --------------------------------------------------------------------------
#> complexity model winner D_hat eff_num_param DIC Delta_DIC
#> more gplm -42.7732 6.0631 -30.6470 0.8498
#> less gplm0 <--- -42.4880 6.3454 -29.7972
#>
#> Comparison 2 Results:
#> --------------------------------------------------------------------------
#> complexity model winner D_hat eff_num_param DIC Delta_DIC
#> more plm -11.3400 2.9739 -5.3923 -0.3058
#> less plm0 <--- -11.4712 2.8865 -5.6982
#>
#> Comparison 3 Results:
#> --------------------------------------------------------------------------
#> complexity model winner D_hat eff_num_param DIC Delta_DIC
#> more gplm0 <--- -42.4880 6.3454 -29.7972 24.0991
#> less plm0 -11.4712 2.8865 -5.6982
#>
#> === End of Summary ===
# Re-run summary with different winning criteria
summary(t_obj, winning_criteria = "Delta_WAIC > 3")
#>
#> === Tournament Model Comparison Summary ===
#>
#> Method: WAIC
#> Winning Criteria: Delta_WAIC > 3
#> Overall Winner: gplm0
#>
#> Comparison 1 Results:
#> --------------------------------------------------------------------------------------------------
#> complexity model winner lppd eff_num_param WAIC SE_WAIC Delta_WAIC SE_Delta_WAIC
#> more gplm 20.7794 6.8706 -27.8176 11.8918 0.5570 0.2416
#> less gplm0 <--- 20.3710 6.7406 -27.2606 12.0360
#>
#> Comparison 2 Results:
#> --------------------------------------------------------------------------------------------------
#> complexity model winner lppd eff_num_param WAIC SE_WAIC Delta_WAIC SE_Delta_WAIC
#> more plm 5.5842 4.2574 -2.6536 6.6635 -0.4066 0.1904
#> less plm0 <--- 5.6284 4.0984 -3.0601 6.6931
#>
#> Comparison 3 Results:
#> --------------------------------------------------------------------------------------------------
#> complexity model winner lppd eff_num_param WAIC SE_WAIC Delta_WAIC SE_Delta_WAIC
#> more gplm0 <--- 20.3710 6.7406 -27.2606 12.0360 24.2005 9.1834
#> less plm0 5.6284 4.0984 -3.0601 6.6931
#>
#> === End of Summary ===
# }