Skip to contents

Projections of population growth from demographic model summaries.

Source: R/trajectoriesFromSummary.R
trajectoriesFromSummary.Rd

Get sample trajectories and summaries from a model defined by a list of parameters. These parameters can come fitted Bayesian model using estimateBayesianRates()$parList or be specified arbitrarily. When parameters from a fitted Bayesian model are used, expected outcomes from trajectoriesFromSummary and trajectoriesFromBayesian() are the same, but trajectoriesFromSummary projections do not include variation in interannual variation over time.

Usage

trajectoriesFromSummary(
  replicates,
  N0,
  Rbar,
  Sbar,
  Riv,
  Siv,
  type = "beta",
  cPars = demographyDefaults(),
  doSummary = T,
  returnSamples = T,
  nthin = formals(bboutools::bb_fit_survival)$nthin,
  varPersists = T,
  ...
)

Arguments

replicates

integer. Number of replicate populations.

N0

number or dataframe. Optional. Initial population size(s). If NA (default) then population growth rate is $_t=S_t*(1+cR_t)/s$. If a data frame N0 column is required, and PopulationName column is required if there is more than one row. Additional (optional) variation columns will be used by addN0Variation().

Rbar, Sbar

Mean and standard deviation of R_bar and S_bar over time. See estimateBayesianRates()$parList for expected form.

Riv, Siv

Parameters defining the distribution of interannual variation. See estimateBayesianRates()$parList for expected form.

type

The distribution of interannual variation varies between "beta" or "bbou" model types.

cPars

optional. Parameters for calculating composition survey bias term.

doSummary

logical. Default TRUE. If FALSE returns unprocessed outcomes from caribouPopGrowth. If TRUE returns summaries and (if returnSamples = T) sample trajectories from prepareTrajectories.

returnSamples

logical. If FALSE returns only summaries. If TRUE returns example trajectories as well.

nthin

integer. The number of the thinning rate.

varPersists

logical. If FALSE treats all variation as interannual variation.

...

Additional arguments passed to caribouPopGrowth

Value

If doSummary is TRUE and returnSamples is TRUE a list with elements:

  • summary: a data.frame mean, lower (2.5%) and upper (97.5%) for each metric.

  • samples: a data.frame providing the full range of trajectories from the model. It is in a long format where "Amount" gives the value for each metric in c, survival, recruitment, X, N, lambda, Sbar, Rbar, Xbar, and lambda_bar, with a row for each combination of "MetricTypeID", "Replicate", "Year", "LambdaPercentile" and PopulationName.

  • surv_data and recruit_data: data.frames with recruitment and survival data

  • popInfo: data.frame of population information including N0, PopulationName and c.

If doSummary is FALSE a data.frame with the output from caribouPopGrowth()

Details

TODO explain varPersists and adjust.mu and adjust.sd

See also

Caribou demography functions: addN0Variation(), bayesianScenariosWorkflow(), bayesianTrajectoryWorkflow(), betaNationalPriors(), caribouPopGrowth(), compareTrajectories(), compositionBiasCorrection(), convertTrajectories(), dataFromSheets(), demographicProjectionApp(), estimateBayesianRates(), estimateNationalRate(), getNationalCoefficients(), getScenarioDefaults(), plotCompareTrajectories(), plotSurvivalSeries(), plotTrajectories(), popGrowthTableJohnsonECCC, simulateObservations(), trajectoriesFromBayesian(), trajectoriesFromNational(), trajectoriesFromSummaryForApp()

Examples

# trajectories from arbitrary demographic rates

traj <- trajectoriesFromSummary(replicates = 35, N0 = 100,
                        Rbar = data.frame(mean = 0.19, sd = 0.23, lower = 0.13,
                                          upper = 0.27, Annual = 2010:2015, Year = 2010:2015,
                                          PopulationName = "A"),
                        Sbar = data.frame(mean = 0.94, sd = 0.61, lower = 0.86,
                                          upper = 0.98, Annual = 2010:2015, Year = 2010:2015,
                                          PopulationName = "A"),
                        Riv = data.frame(R_iv_mean = 0.36, R_iv_shape = 2),
                        Siv = data.frame(S_iv_mean = 0.63, S_iv_shape = 1.4),
                        type = "bbou")
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 0
#>    Unobserved stochastic nodes: 14
#>    Total graph size: 111
#> 
#> Initializing model
#> 
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 0
#>    Unobserved stochastic nodes: 14
#>    Total graph size: 111
#> 
#> Initializing model
#> 
plotTrajectories(traj)
#> Warning: Removed 210 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 6 rows containing missing values or values outside the scale range
#> (`geom_ribbon()`).
#> Warning: Removed 6 rows containing missing values or values outside the scale range
#> (`geom_line()`).