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example_output

Source: R/example_output.R
example_output.Rd

A dataframe in the format output from running run_sims. This exists solely for the purpose of making documentation of visualization functions easier.

  • parameter. Parameters (lambda, psi, and theta for a two species assemblage).

  • Mean. The posterior mean.

  • SD. The standard deviation of the draws of reach parameter.

  • Naive SE. The standard error, not accounting for the correlation in draws

  • Time-series SE. The standard error, accounting for correlation in draws.

  • quantiles. 2.5%, 25%, 50%, 75% and 97.5% quantiles.

  • Rhat. The Gelman-Rubin statistic for each parameter.

  • ess_bulk. The effective sample size in the bulk of the distribution.

  • ess_tail. The effective sample size in the tails of the distribution.

  • truth. The known true data generating value.

  • capture. Did the 95% posterior interval contain the true value?

  • converge. Was the Gelman-Rubin statistic near 1?

  • theta_scenario. The ID for the classifier scenario.

  • scenario. The index of the validation scenario.

  • dataset. The dataset index.

Format

A dataframe containing the columns described below.

Source

A complete small-scale simulation study was run using the functions in ValidationExplorer.

Examples


head(example_output)
#>     parameter       Mean         SD     Naive SE Time-series SE      2.5%
#> 1   lambda[1] 10.5402608 0.44750142 0.0081702207   0.0182798878 9.5979341
#> 2   lambda[2]  1.9680659 0.14992411 0.0027372272   0.0058588973 1.7054354
#> 3      psi[1]  0.3751644 0.08347440 0.0015240271   0.0014985897 0.2216834
#> 4      psi[2]  0.6573396 0.08548119 0.0015606659   0.0016423863 0.4788996
#> 5 theta[1, 1]  0.9037891 0.01331254 0.0002430526   0.0003714269 0.8760539
#> 6 theta[2, 1]  0.1843591 0.02909271 0.0005311577   0.0008468697 0.1303132
#>          25%        50%        75%      97.5%     Rhat  ess_bulk  ess_tail
#> 1 10.2557086 10.5491417 10.8531496 11.4303959 1.000988  642.1892  456.9368
#> 2  1.8589258  1.9606380  2.0708147  2.2792265 1.004294  658.9695  761.9010
#> 3  0.3167012  0.3718161  0.4315794  0.5438603 1.000925 3210.6616 2850.9173
#> 4  0.5996358  0.6603728  0.7181591  0.8100538 1.002008 2478.4785 3027.4949
#> 5  0.8947687  0.9042210  0.9135142  0.9275596 1.002674 1296.4370 1300.5181
#> 6  0.1633648  0.1840185  0.2031841  0.2435565 1.003303 1161.4434 1276.9287
#>   truth capture converge theta_scenario scenario dataset
#> 1 11.00       1        1              1        1       1
#> 2  2.00       1        1              1        1       1
#> 3  0.30       1        1              1        1       1
#> 4  0.60       1        1              1        1       1
#> 5  0.90       1        1              1        1       1
#> 6  0.15       1        1              1        1       1

visualize_parameter_group(
  example_output,
  pars = "lambda",
  theta_scenario = "1",
  scenarios = 1:2
)