make.population.description: Creates a Population.Description object

In dsims: Distance Sampling Simulations

Creates a Population.Description object

Description

Creates an object which describes a population. The values in this object will be used to create instances of the population.

Usage

make.population.description(
  region = make.region(),
  density = make.density(),
  covariates = list(),
  N = numeric(0),
  fixed.N = TRUE
)

Arguments

region	the Region object in which this population exists (see make.region).
density	the Density object describing the distribution of the individuals / clusters (see make.density).
covariates	Named list with one named entry per individual-level covariate. Cluster sizes can be defined here, it must be named 'size'. The distribution of covariate values can either be defined by specifying a particular distribution and its parameters or as a discrete distribution in a dataframe. Dataframes should have columns level and prob (and optionally strata) specifying the covariates levels, probabilities and strata if they are strata specific. Distributions can be defined as lists with named entries distribution and the relevant parameters as specified in details. A list of distributions can be provided with one for each strata.
N	the number of individuals / clusters in a population with one value per strata. Total population size is 1000 by default.
fixed.N	a logical value. If TRUE the population is generated from the value(s) of N otherwise it is generated from the values in the density grid.

Details

Individual-level covariate values can be defined as one of the following distributions: 'normal', 'poisson', 'ztruncpois' or 'lognormal'. The distribution name and the associated parameters as defined in the table below must be provided in a named list. Either one list can be provided for the entire study area or multiple lists grouped together as a list with one per strata.

Distribution	Parameters
normal	mean	sd
poisson	lambda
ztruncpois	mean
lognormal	meanlog	sdlog

Value

Population.Description-class

Author(s)

Laura Marshall

See Also

make.simulation make.detectability make.density

Examples

# Create a basic rectangular study area
region <- make.region()

# Make a density grid (large spacing for speed)
density <- make.density(region = region,
                        x.space = 200,
                        y.space = 100,
                        constant = 1)
density <- add.hotspot(density, centre = c(1000, 100), sigma = 250, amplitude = 10)

# Define some covariate values for out population
covs <- list()
covs$size <- list(distribution = "ztruncpois", mean = 5)

# Define the population description
popdsc <- make.population.description(region = region,
                                      density = density,
                                      covariates = covs,
                                      N = 200)

# define the detecability
detect <- make.detectability(key.function = "hn", scale.param = 25, truncation = 50)

# generate an example population
pop <- generate.population(popdsc, region = region, detectability = detect)

plot(pop, region)

# Multi-strata example (make sf shape)
s1 = matrix(c(0,0,0,2,1,2,1,0,0,0),ncol=2, byrow=TRUE)
s2 = matrix(c(1,0,1,2,2,2,2,0,1,0),ncol=2, byrow=TRUE)
pol1 = sf::st_polygon(list(s1))
pol2 = sf::st_polygon(list(s2))
sfc <- sf::st_sfc(pol1,pol2)
strata.names <- c("low", "high")
sf.pol <- sf::st_sf(strata = strata.names, geom = sfc)

region <- make.region(region.name = "Multi-strata Eg",
                      strata.name = strata.names,
                      shape = sf.pol)

density <- make.density(region = region,
                        x.space = 0.22,
                        constant = c(10,80))

covs <- list()
covs$size <- list(list(distribution = "poisson", lambda = 25),
                  list(distribution = "poisson", lambda = 15))
covs$sex <- data.frame(level = rep(c("male", "female"),2),
                      prob = c(0.5, 0.5, 0.6, 0.4),
                      strata = c(rep("low",2),rep("high",2)))

# Define the population description (this time using the density to determine
# the population size)
popdesc <- make.population.description(region = region,
                                       density = density,
                                       covariates = covs,
                                       fixed.N = FALSE)

# define the detecability (see make.detectability to alter detection function
# for different covariate values)
detect <- make.detectability(key.function = "hn", scale.param = 25, truncation = 50)

# generate an example population
pop <- generate.population(popdesc, region = region, detectability = detect)

plot(pop, region)

dsims documentation built on May 29, 2024, 12:10 p.m.