Define scenarios in a table and simulateObservations()
, run the
caribouBayesianPM()
model and getOutputTables()
for each scenario.
Usage
runScnSet(
scns,
simInitial,
ePars = list(collarOnTime = 4, collarOffTime = 4, 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
getSimsInitial()
- 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
a list of model priors. If disturbance is NA, this should be list(priors_survival=c(...),priors_recruitment=c(...)); see
bboutools::bb_priors_survival
andbboutools::bb_priors_recruitment
for details. If disturbance is not NA, seegetPriors()
for details.- niters
A whole number of 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
caribouBayesianPM()
.
Value
A list similar to getOutputTables()
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:
bbouMakeSummaryTable()
,
caribouBayesianPM()
,
caribouPopGrowth()
,
caribouPopSimMCMC()
,
compositionBiasCorrection()
,
demographicCoefficients()
,
demographicProjectionApp()
,
demographicRates()
,
doSim()
,
getBBNationalInformativePriors()
,
getOutputTables()
,
getPriors()
,
getScenarioDefaults()
,
getSimsInitial()
,
getSimsNational()
,
plotRes()
,
popGrowthTableJohnsonECCC
,
simulateObservations()
Examples
scns <- expand.grid(
obsYears =c(10, 20), collarCount = c(30, 300), cowMult = 2, collarInterval = 2,
iAnthro = 0,
obsAnthroSlope = 0, projAnthroSlope = 0, sQuantile = 0.9,
rQuantile = 0.7, N0 = 1000
)
eParsIn <- list(collarOnTime = 4, collarOffTime = 4, collarNumYears = 3)
simsIn <- getSimsInitial()
#> Using saved object
scResults <- runScnSet(scns, simsIn, eParsIn,
niters = 10)# only set to speed up example. Normally keep defaults.
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 8
#> Unobserved stochastic nodes: 85
#> Total graph size: 409
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 52
#> Unobserved stochastic nodes: 172
#> Total graph size: 666
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 20
#> Unobserved stochastic nodes: 93
#> Total graph size: 489
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 74
#> Unobserved stochastic nodes: 200
#> Total graph size: 806
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 10
#> Unobserved stochastic nodes: 83
#> Total graph size: 409
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 54
#> Unobserved stochastic nodes: 170
#> Total graph size: 666
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 20
#> Unobserved stochastic nodes: 93
#> Total graph size: 489
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 74
#> Unobserved stochastic nodes: 200
#> Total graph size: 806
#>
#> Initializing model
#>