Get a set of simulation results from fitted demographic models in raw form Assumes that rec_pred and surv_pred each include the same years and populations.TO DO: check this.

Get a set of simulation results from fitted demographic models in raw form

Assumes that rec_pred and surv_pred each include the same years and populations.TO DO: check this.

Format trajectory tables

Get 95% prediction intervals from trajectories

Usage

caribouPopSimMCMC(
  popInfo = NA,
  rec_pred,
  surv_pred,
  initYear = NULL,
  correlateRates = FALSE,
  returnExpected = FALSE,
  c = formals(caribouPopGrowth)$c,
  ...
)

convertTrajectories(pars)

summarizeCaribouPopSim(pars, returnSamples = T)

Arguments

popInfo

If NA (default) predictions are made without populations size, density dependence, or demographic stochasticity. See caribouPopGrowth() for details.

rec_pred

results returned by bb_fit_recruitment() or bb_predict_calf_cow_ratio() functions of bboutools R package, or recruit_fit returned by bbouMakeSummaryTable().

surv_pred

bboufit object return by bb_fit_survival() or bb_predict_survival() functions of bboutools R package, or surv_fit returned by bbouMakeSummaryTable().

initYear

numeric. Initial year.

correlateRates

logical. Set TRUE to force correlation between recruitment and survival. Ignored

returnExpected

logical. Default FALSE. Set TRUE to return expected values of R, S, and lambda (without interannual variation). Ignored if rec_pred/surv_pred are bb_predict_calf_cow_ratio()/bb_predict_survival() results.

c

Number. Bias correction term.

pars

returnSamples

Value

caribouPopSimMCMC: a data frame with results from caribouPopGrowth() for each set of survival/recruitment predictions.

convertTrajectories: formatted tables

summarizeCaribouPopSim:

See also

Caribou demography functions: bbouMakeSummaryTable(), caribouBayesianPM(), caribouPopGrowth(), compositionBiasCorrection(), demographicCoefficients(), demographicProjectionApp(), demographicRates(), doSim(), getBBNationalInformativePriors(), getOutputTables(), getPriors(), getScenarioDefaults(), getSimsInitial(), getSimsNational(), plotRes(), popGrowthTableJohnsonECCC, runScnSet(), simulateObservations()

Caribou demography functions: bbouMakeSummaryTable(), caribouBayesianPM(), caribouPopGrowth(), compositionBiasCorrection(), demographicCoefficients(), demographicProjectionApp(), demographicRates(), doSim(), getBBNationalInformativePriors(), getOutputTables(), getPriors(), getScenarioDefaults(), getSimsInitial(), getSimsNational(), plotRes(), popGrowthTableJohnsonECCC, runScnSet(), simulateObservations()

Examples

# \donttest{
  # Note these examples take a long time to run!
  mod <- bbouMakeSummaryTable(
    surv_data = bboudata::bbousurv_a,
    recruit_data = bboudata::bbourecruit_a, 
    N0 = NA, return_mcmc = TRUE
  )
                           
  outmcmc = caribouPopSimMCMC(popInfo=NA,mod$recruit_fit,mod$surv_fit)
  names(outmcmc)
#>  [1] "N0"                  "lambda"              "lambdaE"            
#>  [4] "N"                   "R_t"                 "X_t"                
#>  [7] "S_t"                 "n_recruits"          "surviving_adFemales"
#> [10] "lab"                 "Year"                "PopulationName"     
#> [13] "id"                 

  #get 95% prediction intervals from demographic trajectories
  PImcmc <- summarizeCaribouPopSim(convertTrajectories(outmcmc))
  str(PImcmc, max.level = 3, give.attr = FALSE)
#> List of 2
#>  $ summary:'data.frame':	189 obs. of  8 variables:
#>   ..$ MetricTypeID  : chr [1:189] "N" "N" "N" "N" ...
#>   ..$ Year          : num [1:189] 1989 2012 1995 1997 2001 ...
#>   ..$ PopulationName: chr [1:189] "A" "A" "A" "A" ...
#>   ..$ Mean          : num [1:189] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
#>   ..$ lower         : Named num [1:189] NA NA NA NA NA NA NA NA NA NA ...
#>   ..$ upper         : Named num [1:189] NA NA NA NA NA NA NA NA NA NA ...
#>   ..$ probViable    : num [1:189] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
#>   ..$ Parameter     : chr [1:189] "Female population size" "Female population size" "Female population size" "Female population size" ...
#>  $ samples: tibble [567,000 × 7] (S3: tbl_df/tbl/data.frame)
#>   ..$ Replicate       : chr [1:567000] "x1" "x1" "x1" "x1" ...
#>   ..$ LambdaPercentile: logi [1:567000] NA NA NA NA NA NA ...
#>   ..$ Year            : num [1:567000] 1989 1989 1989 1989 1989 ...
#>   ..$ PopulationName  : chr [1:567000] "A" "A" "A" "A" ...
#>   ..$ Timestep        : num [1:567000] 1989 1989 1989 1989 1989 ...
#>   ..$ MetricTypeID    : chr [1:567000] "c" "survival" "recruitment" "X" ...
#>   ..$ Amount          : num [1:567000] NA 0.8901 0.1397 0.0699 NA ...
# }