PaleoPopResults: R6 class representing paleopop simulator results.
In paleopop: Pattern-Oriented Modeling Framework for Coupled Niche-Population Paleo-Climatic Models

PaleoPopResults

R Documentation

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

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

paleopop documentation built on Oct. 11, 2023, 5:14 p.m.