paleopop (version 2.1.7)

PaleoPopResults: R6 class representing paleopop simulator results.

Description

R6 class for encapsulating and dynamically generating spatially-explicit paleopop_simulator results, as well as optional re-generated Generator for niche carrying capacity and/or human density.

Super classes

poems::GenericClass -> poems::GenericModel -> poems::SpatialModel -> poems::SimulationResults -> PaleoPopResults

Public fields

attached

A list of dynamically attached attributes (name-value pairs).

Active bindings

model_attributes

A vector of model attribute names.

region

A Region (or inherited class) object specifying the study region.

coordinates

Data frame (or matrix) of X-Y population coordinates (WGS84) in longitude (degrees West) and latitude (degrees North).

time_steps

Number of simulation time steps.

burn_in_steps

Optional number of initial 'burn-in' time steps to be ignored.

occupancy_mask

Optional binary mask array (matrix), data frame, or raster (stack) for each cell at each time-step of the simulation including burn-in.

trend_interval

Optional time-step range (indices) for trend calculations (assumes indices begin after the burn-in when utilized).

abundance

Matrix of population abundance across simulation time-steps (populations rows by duration columns).

abundance_trend

Trend or average Sen's slope of total abundance (optionally across a time-step interval).

ema

Matrix of population expected minimum abundance (EMA) across simulation time-steps (populations rows by duration columns).

extirpation

Array of population extirpation times.

extinction_location

The weighted centroid of cells occupied in the time-step prior to the extirpation of all populations (if occurred).

harvested

Matrix of the number of animals harvested from each population at each time-step (populations rows by duration columns).

occupancy

Array of the number of populations occupied at each time-step.

carrying_capacity

Optional matrix of simulation input carrying capacity to be combined with results (populations rows by duration columns).

human_density

Optional matrix of simulation input human density to be combined with results (populations rows by duration columns).

all

Nested simulation results for all cells.

parent

Parent simulation results for individual cells.

default

Default value/attribute utilized when applying primitive metric functions (e.g. max) to the results.

attribute_aliases

A list of alternative alias names for model attributes (form: alias = "attribute") to be used with the set and get attributes methods.

error_messages

A vector of error messages encountered when setting model attributes.

warning_messages

A vector of warning messages encountered when setting model attributes.

Methods

Public methods

• PaleoPopResults$clone()

Inherited methods

• poems::GenericModel$get_attribute()
• poems::GenericModel$get_attribute_aliases()
• poems::SimulationResults$get_attribute_names()
• poems::SimulationResults$get_attributes()
• poems::SimulationResults$initialize()
• poems::SimulationResults$new_clone()
• poems::SimulationResults$set_attributes()

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Method clone()

The objects of this class are cloneable with this method.

Usage

PaleoPopResults$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(raster)
library(poems)
# Ring Island example region
coordinates <- data.frame(x = rep(seq(-178.02, -178.06, -0.01), 5),
                          y = rep(seq(19.02, 19.06, 0.01), each = 5))
template_raster <- Region$new(coordinates = coordinates)$region_raster # full extent
sealevel_raster <- template_raster
template_raster[][c(7:9, 12:14, 17:19)] <- NA # make Ring Island
sealevel_raster[][c(7:9, 12:14, 17:18)] <- NA
raster_stack <- raster::stack(x = append(replicate(9, template_raster), sealevel_raster))
region <- PaleoRegion$new(template_raster = raster_stack)

# Model template
model_template <- PaleoPopModel$new(
region = region,
time_steps = 10,
years_per_step = 12, # years per generational time-step
standard_deviation = 0.1,
growth_rate_max = 0.6,
harvest = FALSE,
populations = region$region_cells,
initial_abundance = seq(9000, 0, -1000),
transition_rate = 1.0,
carrying_capacity = rep(1000, 17),
dispersal = (!diag(nrow = 17, ncol = 17))*0.05,
density_dependence = "logistic",
dispersal_target_k = 10,
occupancy_threshold = 1, 
abundance_threshold = 10,
results_selection = c("abundance")
)

# Simulations
results <- paleopop_simulator(model_template)

# Results
results_model <- PaleoPopResults$new(results = results, region = region, time_steps = 10)
results_model$extirpation # cells where the population goes to zero are marked 1
results_model$occupancy # indicates with 0 and 1 which cells are occupied at each time step
results_model$ema # expected minimum abundance