Get a list of the pages of a report on a discharge rating curve model or tournament
Usage
get_report_pages(x, type = 1)
# S3 method for class 'plm0'
get_report_pages(x, type = 1)
# S3 method for class 'plm'
get_report_pages(x, type = 1)
# S3 method for class 'gplm0'
get_report_pages(x, type = 1)
# S3 method for class 'gplm'
get_report_pages(x, type = 1)
# S3 method for class 'tournament'
get_report_pages(x, type = 1)
Arguments
- x
An object of class "tournament", "plm0", "plm", "gplm0" or "gplm".
- type
An integer denoting what type of report is to be produced. Defaults to type 1. Possible types are:
1
Produces a report displaying the results of the model (winning model if a tournament provided). The first page contains a panel of four plots and a summary of the posterior distributions of the parameters. On the second page a tabular prediction of discharge on an equally spaced grid of stages is displayed. This prediction table can span multiple pages.
2
Produces a ten page report and is only permissible for objects of class "tournament". The first four pages contain a panel of four plots and a summary of the posterior distributions of the parameters for each of the four models in the tournament, the fifth page shows a summary of the tournament model comparison, the sixth page convergence diagnostics plots, and the final four pages shows the histograms of the parameters in each of the four models.
Methods (by class)
get_report_pages(plm0)
: Get report pages for plm0 model objectget_report_pages(plm)
: Get report pages for plm model objectget_report_pages(gplm0)
: Get report pages for gplm0 model objectget_report_pages(gplm)
: Get report pages for gplm model objectget_report_pages(tournament)
: Get report pages for discharge rating curve tournament model object
See also
tournament
for running a tournament, summary.tournament
for summaries and get_report
for generating and saving a report of a tournament object.
Examples
# \donttest{
data(krokfors)
set.seed(1)
plm0.fit <- plm0(formula=Q~W,data=krokfors,num_cores=2)
#> Progress:
#> Initializing Metropolis MCMC algorithm...
#> Multiprocess sampling (4 chains in 2 jobs) ...
#>
#> MCMC sampling completed!
#>
#> Diagnostics:
#> Acceptance rate: 36.09%.
#> ✔ All chains have mixed well (Rhat < 1.1).
#> ✔ Effective sample sizes sufficient (eff_n_samples > 400).
plm0_pages <- get_report_pages(plm0.fit)
# }