make.simulation: Creates a Simulation object
In dsims: Distance Sampling Simulations

make.simulation

R Documentation

Creates a Simulation object

Description

This creates a simulation with all the information necessary for dsims to generate a population, create transects, simulate the survey process and fit detection functions and estimate density / abundance. This function can be used by itself based on default values to create a simple line transect example, see Examples below. To create more complex simulations it is advisable to define the different parts of the simulation individually before grouping them together. See the Arguments for links to the functions which make the definitions for the individual simulation components. For a more in depth example please refer to the 'GettingStarted' vignette.

Usage

make.simulation(
  reps = 10,
  design = make.design(),
  population.description = make.population.description(),
  detectability = make.detectability(),
  ds.analysis = make.ds.analysis()
)

Arguments

`reps`	number of times the simulation should be repeated
`design`	an object of class Survey.Design created by a call to make.design
`population.description`	an object of class Population.Description created by a call to make.population.description
`detectability`	and object of class Detectability created by a call to make.detectability
`ds.analysis`	an objects of class DS.Analysis created by a call to make.ds.analysis

Details

The make.simulation function is now set up so that by default (with the exception of specifying point transects rather than line) it can run a simple simulation example. See examples.

Value

Simulation-class object

Author(s)

Laura Marshall

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 = 300,
                        y.space = 100,
                        constant = 1)
density <- add.hotspot(density, centre = c(1000, 100), sigma = 250, amplitude = 10)

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

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

# Define the design
design <- make.design(region = region,
                      transect.type = "line",
                      design = "systematic",
                      samplers = 20,
                      design.angle = 0,
                      truncation = 50)

# Define the analyses
ds.analyses <- make.ds.analysis(dfmodel = ~1,
                                key = "hn",
                                truncation = 50,
                                criteria = "AIC")

# Put all the components together in the simulation (note no. of replicates
# reps = 1 is only for a single test run and should be 999 or more to be able
# to draw inference.)
simulation <- make.simulation(reps = 1,
                              design = design,
                              population.description = popdsc,
                              detectability = detect,
                              ds.analysis = ds.analyses)

# run an example survey to check the setup
survey <- run.survey(simulation)
plot(survey, region)

# Run the simulation
# Warning: if you have increased the number of replications then it can take a
# long time to run!
simulation <- run.simulation(simulation)
summary(simulation)

# For a more in depth example please look at
vignette("GettingStarted", 'dsims')

dsims documentation built on Aug. 30, 2022, 5:06 p.m.