R/getSimsNational.R
getSimsNational.RdGet a set of simulation results from the national demographic model
getSimsNational(
replicates = 1000,
N0 = 1000,
Anthro = seq(0, 100, by = 1),
fire_excl_anthro = 0,
useQuantiles = NULL,
populationGrowthTable = NULL,
adjustR = TRUE,
cPars = getScenarioDefaults(),
forceUpdate = F,
interannualVar = eval(formals(caribouPopGrowth)$interannualVar)
)integer. Number of replicate populations.
initial population size
numeric. A vector of numbers between 0 and 100
representing the percentage of the landscape covered by anthropogenic
disturbance buffered by 500 m, and the percentage covered by fire that does
not overlap anthropogenic disturbance. The two vectors will be combined
with expand.grid() to give the set of scenarios simulated.
logical or numeric. If it is a numeric vector it must be
length 2 and give the low and high limits of the quantiles to use. If
useQuantiles != FALSE, each replicate population is assigned to a
quantile of the distribution of variation around the expected values, and
remains in that quantile as covariates change. If useQuantiles = TRUE,
replicate populations will be assigned to quantiles in the default range of
0.025 and 0.975.
data.frame.popGrowthTableJohnsonECCC is included in the package and should be used in most cases. A custom table of model coefficients and standard errors or confidence intervals can be provided but it must match the column names of popGrowthTableJohnsonECCC. If the table does not contain the standard error it is calculated from the confidence interval.
Logical. Adjust R to account for delayed age at first reproduction (DeCesare et al. 2012; Eacker et al. 2019).
optional. Parameters for calculating composition survey bias term.
logical. If the default inputs are used the result is
cached. Set forceUpdate to TRUE to ensure the simulations are re-run.
list or logical. List containing interannual
variability parameters. These can be either coefficients of variation
(R_CV, S_CV) or beta precision parameters (R_phi, S_phi). Set to FALSE to
ignore interannual variability.
a list with two elements:
summary: a tibble with a summary of parameter values for each scenario. Column names are Anthro, Mean, lower, upper, Parameter.
samples: a tibble with parameter values for each scenario and replicate 4 rows per replicate \* scenario. Column names are Anthro, Parameter and Value
Caribou demography functions:
caribouBayesianPM(),
caribouPopGrowth(),
compositionBiasCorrection(),
demographicCoefficients(),
demographicProjectionApp(),
demographicRates(),
getOutputTables(),
getPriors(),
getScenarioDefaults(),
plotRes(),
popGrowthTableJohnsonECCC,
runScnSet(),
simulateObservations()
getSimsNational()
#> Using saved object
#> $summary
#> # A tibble: 505 × 5
#> Anthro Mean lower upper Parameter
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 0 0.882 0.689 0.998 Adult female survival
#> 2 1 0.877 0.695 0.998 Adult female survival
#> 3 2 0.874 0.677 0.998 Adult female survival
#> 4 3 0.873 0.686 0.995 Adult female survival
#> 5 4 0.875 0.686 0.997 Adult female survival
#> 6 5 0.872 0.686 0.997 Adult female survival
#> 7 6 0.874 0.673 0.997 Adult female survival
#> 8 7 0.868 0.680 0.996 Adult female survival
#> 9 8 0.868 0.668 0.995 Adult female survival
#> 10 9 0.867 0.677 0.996 Adult female survival
#> # ℹ 495 more rows
#>
#> $samples
#> # A tibble: 505,000 × 3
#> Anthro Parameter Value
#> <dbl> <chr> <dbl>
#> 1 0 Adult female survival 0.915
#> 2 0 Recruitment 0.328
#> 3 0 Adjusted recruitment 0.149
#> 4 0 Population growth rate 0.960
#> 5 0 Female population size 827
#> 6 1 Adult female survival 0.848
#> 7 1 Recruitment 0.269
#> 8 1 Adjusted recruitment 0.126
#> 9 1 Population growth rate 1.01
#> 10 1 Female population size 1040
#> # ℹ 504,990 more rows
#>