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Default parameters for simulation of example demographic trajectories.

Source: R/getScenarioDefaults.R
getScenarioDefaults.Rd

Returns default parameter values for simulations of example demographic trajectories. See simulateObservations() for additional details.

Usage

getScenarioDefaults(
  paramTable = NULL,
  iFire = 0,
  iAnthro = 0,
  obsAnthroSlope = 2,
  projAnthroSlope = 2,
  rSlopeMod = 1,
  sSlopeMod = 1,
  lQuantile = NA,
  sQuantile = NA,
  rQuantile = NA,
  correlateRates = F,
  projYears = 35,
  obsYears = 15,
  preYears = 0,
  N0 = 1000,
  qMin = 0,
  qMax = 0,
  uMin = 0,
  uMax = 0,
  zMin = 0,
  zMax = 0,
  cowMult = 6,
  collarInterval = NA,
  cowCount = NA,
  collarCount = NA,
  startYear = NA,
  interannualVar = list(eval(formals(caribouPopGrowth)$interannualVar)),
  curYear = 2023
)

Arguments

paramTable

a data.frame with column names matching the arguments below. Any columns that are missing will be filled with the default values.

iFire

number. Initial fire disturbance percentage.

iAnthro

number. Initial anthropogenic disturbance percentage

obsAnthroSlope

number. Percent change in anthropogenic disturbance per year in the observation period

projAnthroSlope

number. Percent change in anthropogenic disturbance per year in the projection period

rSlopeMod

number. Disturbance-recruitment slope multiplier

sSlopeMod

number. Disturbance-survival slope multiplier

lQuantile

number in 0, 1. Lambda quantile

sQuantile

number in 0,1. Survival quantile.

rQuantile

number in 0,1. Recruitment quantile.

correlateRates

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

projYears

Number of years of projections

obsYears

Number of years of observations

preYears

Number of years before monitoring begins

N0

Number or vector of numbers. Initial population size for one or more sample populations. If NA then population growth rate is $_t=S_t*(1+cR_t)/s$.

qMin

number in 0, 1. Minimum ratio of bulls to cows in composition survey groups.

qMax

number in 0, 1. Maximum ratio of bulls to cows in composition survey groups.

uMin

number in 0, 1. Minimum probability of misidentifying young bulls as adult females and vice versa in composition survey.

uMax

number in 0, 1. Maximum probability of misidentifying young bulls as adult females and vice versa in composition survey.

zMin

number in 0, 1. Minimum probability of missing calves in composition survey.

zMax

number in 0, <1. Maximum probability of missing calves in composition survey.

cowMult

number >= 1. The apparent number of adult females per collared animal in composition survey. Set to NA to use cowCount.

collarInterval

number. Optional. Number of years between collar deployments. If missing assumed to be every year

cowCount

Optional. Only used in runScnSet() to set the number of cows per year in recruitment survey

collarCount

number >= 1. The target number of collars active each year. Set to NA to use freqStartsPerYear in simulateObservations()

startYear

year. First year in observation period. Optional, if not provided it will be calculated from curYear and obsYears

interannualVar

list or logical. List containing interannual variability parameters. These can be either coefficients of variation (R_CV, S_CV), beta precision parameters (R_phi, S_phi), or random effects parameters from a logistic glmm (R_annual, S_annual). Set to FALSE to ignore interannual variability.

curYear

year. The current year. All years before are part of the observation period and years after are part of the projection period.

Value

a data.frame of parameter values including a label that combines all the parameter names and values into a string

See also

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

Examples

getScenarioDefaults()
#> # A tibble: 1 × 23
#>   iFire iAnthro obsAnthroSlope projAnthroSlope rSlopeMod sSlopeMod
#>   <dbl>   <dbl>          <dbl>           <dbl>     <dbl>     <dbl>
#> 1     0       0              2               2         1         1
#> # ℹ 17 more variables: correlateRates <lgl>, projYears <dbl>, obsYears <dbl>,
#> #   preYears <dbl>, N0 <dbl>, qMin <dbl>, qMax <dbl>, uMin <dbl>, uMax <dbl>,
#> #   zMin <dbl>, zMax <dbl>, cowMult <dbl>, interannualVar <list>,
#> #   curYear <dbl>, ID <int>, label <chr>, startYear <dbl>

# paramTable list takes precedence over argument values
getScenarioDefaults(paramTable = data.frame(iFire = 10, iAnthro = 20, obsYears = 1), obsYears = 5)
#>   iFire iAnthro obsAnthroSlope projAnthroSlope rSlopeMod sSlopeMod
#> 1    10      20              2               2         1         1
#>   correlateRates projYears obsYears preYears   N0 qMin qMax uMin uMax zMin zMax
#> 1          FALSE        35        1        0 1000    0    0    0    0    0    0
#>   cowMult   interannualVar curYear ID
#> 1       6 0.46000, 0.08696    2023  1
#>                                                                                                                                                                                                                                               label
#> 1 ID1_curYear2023_interannualVarlist(R_CV = 0.46, S_CV = 0.08696)_cowMult6_zMax0_zMin0_uMax0_uMin0_qMax0_qMin0_N01000_preYears0_obsYears1_projYears35_correlateRatesFALSE_sSlopeMod1_rSlopeMod1_projAnthroSlope2_obsAnthroSlope2_iAnthro20_iFire10_
#>   startYear
#> 1      2023