Plot method for a discharge rating curve tournament

Compare the four models from the tournament object in multiple ways

Usage

# S3 method for class 'tournament'
plot(x, type = "tournament_results", transformed = FALSE, ...)

Arguments

x

An object of class "tournament"

type

A character denoting what type of plot should be drawn. Possible types are:

boxplot

Creates a boxplot of the posterior log-likelihood values, on the deviance scale.

rating_curve

Plots the rating curve.

rating_curve_mean

Plots the posterior mean of the rating curve.

f

Plots the power-law exponent.

sigma_eps

Plots the standard deviation on the data level.

residuals

Plots the log residuals.

tournament_results

Plots a diagram showing the tournament results.

transformed

A logical value indicating whether the quantity should be plotted on a transformed scale used during the Bayesian inference. Defaults to FALSE.

...

Not used in this function

Value

No return value, called for side effects

See also

tournament to run a discharge rating curve tournament and summary.tournament for summaries.

Examples

# \donttest{
data(krokfors)
set.seed(1)
t_obj <- tournament(formula = Q ~ W, data = 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%
plot(t_obj)
plot(t_obj, transformed = TRUE)

plot(t_obj, type = 'boxplot')
plot(t_obj, type = 'f')
plot(t_obj, type = 'sigma_eps')
plot(t_obj, type = 'residuals')
plot(t_obj, type = 'tournament_results')

# }