Skip to contents

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 0.1892343 0.08051724 0.3142993   24.72798   0.1662202
#> 2              B 0.2038585 0.10390811 0.3142993   24.72798   0.1721851
#>   R_bar_upper     S_bar      S_sd S_iv_mean S_iv_shape S_bar_lower S_bar_upper
#> 1   0.2143099 0.8821120 0.2395324 0.4957008   13.21386   0.8272942   0.9247314
#> 2   0.2387083 0.9062028 0.2841799 0.4957008   13.21386   0.8495475   0.9460866
#>    N0 nCollarYears nSurvYears nCowsAllYears nRecruitYears
#> 1 500          900         31          2353            27
#> 2 500          519         18          2001            15