Run the Bayesian population model for multiple parameter sets

Define scenarios in a table and simulateObservations(), run the bayesianTrajectoryWorkflow() model and compareTrajectories() for each scenario.

Usage

bayesianScenariosWorkflow(
  scns,
  simInitial,
  ePars = list(collarOnTime = 4, collarOffTime = 3, collarNumYears = 4),
  Rep = NULL,
  printProgress = FALSE,
  priors = "default",
  niters = formals(bboutools::bb_fit_survival)$niters,
  nthin = formals(bboutools::bb_fit_survival)$nthin,
  returnSamples = F,
  ...
)

Arguments

scns

data.frame. Parameters for the simulations. See getScenarioDefaults() for details.

simInitial

Initial simulation results, produced by calling trajectoriesFromNational(), trajectoriesFromBayesian(), or trajectoriesFromSummary()

ePars

list. Additional parameters passed on to simulateObservations()

Rep

integer. Optional. If specified, select specified replicate trajectory.

printProgress

logical. Should the scenario number and parameters be printed at each step?

priors

list. Optional. If disturbance is NULL, this should be list(priors_survival=c(...),priors_recruitment=c(...)); see bboutools::bb_priors_survival() and bboutools::bb_priors_recruitment() for details. If disturbance is not NULL, see betaNationalPriors() for details.

niters

integer. The number of iterations per chain after thinning and burn-in.

nthin

integer. The number of the thinning rate.

returnSamples

logical. Optional. If true, return full results from bayesianTrajectoryWorkflow().

...

Other parameters passed on to bboutools::bb_fit_survival() and bboutools::bb_fit_recruitment().

Value

A list similar to compareTrajectories() where tables for each scenario have been appended together. Plus an error log for any scenarios that failed to run.

See also

Caribou demography functions: addMissingYears(), addN0Variation(), bayesianTrajectoryWorkflow(), betaNationalPriors(), caribouPopGrowth(), compareTrajectories(), compositionBiasCorrection(), convertTrajectories(), dataFromSheets(), demographicProjectionApp(), estimateBayesianRates(), estimateNationalRate(), getCaribouYear(), getNationalCoefficients(), getScenarioDefaults(), plotCompareTrajectories(), plotSurvivalSeries(), plotTrajectories(), popGrowthTableJohnsonECCC, simulateObservations(), trajectoriesFromBayesian(), trajectoriesFromNational(), trajectoriesFromSummary(), trajectoriesFromSummaryForApp()

Examples

scns <- expand.grid(
  obsYears =c(10, 20), collarCount = c(30, 300), cowMult = 2, collarInterval = 2,
  iAnthro = 0,
  obsAnthroSlope = 0, projAnthroSlope = 0, sQuaMntile = 0.9,
  rQuantile = 0.7, N0 = 1000
)

eParsIn <- list(collarOnTime = 4, collarOffTime = 4, collarNumYears = 3)
simsIn <- trajectoriesFromNational()
#> Updating cached initial simulations.
scResults <- bayesianScenariosWorkflow(scns, simsIn, eParsIn,
                       niters = 10)# only set to speed up example. Normally keep defaults.
#> Registered S3 method overwritten by 'mcmcr':
#>   method         from 
#>   as.mcmc.nlists nlist
#> Registered S3 method overwritten by 'rjags':
#>   method               from 
#>   as.mcmc.list.mcarray mcmcr
#> Warning: Adaptation incomplete
#> NOTE: Stopping adaptation
#> 
#> 
#> Warning: Adaptation incomplete
#> NOTE: Stopping adaptation
#> 
#> 
#> Warning: Adaptation incomplete
#> NOTE: Stopping adaptation
#> 
#> 
#> Warning: Adaptation incomplete
#> NOTE: Stopping adaptation
#> 
#> 
#> Warning: Adaptation incomplete
#> NOTE: Stopping adaptation
#> 
#> 
#> Warning: Adaptation incomplete
#> NOTE: Stopping adaptation
#> 
#> 
#> Warning: Adaptation incomplete
#> NOTE: Stopping adaptation
#> 
#> 
#> Warning: Adaptation incomplete
#> NOTE: Stopping adaptation
#> 
#>