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Create summary table of demographic rates from survival and recruitment surveys

Source: R/estimateBayesianRates.R
estimateBayesianRates.Rd

Create summary table of demographic rates from survival and recruitment surveys

Usage

estimateBayesianRates(
  surv_data,
  recruit_data,
  N0 = NA,
  disturbance = NULL,
  priors = NULL,
  shiny_progress = FALSE,
  return_mcmc = FALSE,
  i18n = NULL,
  niters = formals(bboutools::bb_fit_survival)$niters,
  nthin = formals(bboutools::bb_fit_survival)$nthin,
  ...
)

Arguments

surv_data

dataframe. Survival data in bboudata format

recruit_data

dataframe. Recruitment data in bboudata format

N0

dataframe. Optional. Initial population estimates, required columns are PopulationName and N0

disturbance

dataframe. Optional. If provided, fit a Beta model that includes disturbance covariates.

priors

list. Optional. If disturbance is NA, 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 NA, see betaNationalPriors for details.

shiny_progress

logical. Should shiny progress bar be updated. Only set to TRUE if using in an app.

return_mcmc

boolean. If TRUE return fitted survival and recruitment models. Default FALSE.

niters

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

nthin

integer. The number of the thinning rate.

...

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

Value

If return_mcmc is TRUE then a list with results and fitted models, if FALSE just the results summaries are returned.

See also

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

Examples

s_data <- rbind(bboudata::bbousurv_a, bboudata::bbousurv_b)
r_data <- rbind(bboudata::bbourecruit_a, bboudata::bbourecruit_b)
estimateBayesianRates(s_data, r_data, N0 = 500)
#>   PopulationName     R_bar      R_sd R_iv_mean R_iv_shape R_bar_lower
#> 1              A 0.1893446 0.0822676 0.3148171   25.22859   0.1652187
#> 2              B 0.2038394 0.1014211 0.3148171   25.22859   0.1729125
#>   R_bar_upper     S_bar      S_sd S_iv_mean S_iv_shape S_bar_lower S_bar_upper
#> 1   0.2146941 0.8817129 0.2336958 0.4927553   13.06435   0.8273442   0.9230849
#> 2   0.2383758 0.9073243 0.2823650 0.4927553   13.06435   0.8524029   0.9456843
#>    N0 nCollarYears nSurvYears nCowsAllYears nRecruitYears
#> 1 500          900         31          2353            27
#> 2 500          519         18          2001            15