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

Description Usage Arguments Details Value Author(s) Examples

This creates a simulation with all the information necessary for DSsim to generate a population, create or read in 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. Example simulations can also be found at <https://github.com/DistanceDevelopment/DSsim/wiki>.

make.simulation(
  reps = 10,
  single.transect.set = FALSE,
  double.observer = FALSE,
  region.obj = make.region(),
  design.obj = make.design(),
  population.description.obj = make.population.description(),
  detectability.obj = make.detectability(),
  ddf.analyses.list = make.ddf.analysis.list()
)

`reps`	number of times the simulation should be repeated
`single.transect.set`	logical specifying whether the transects should be kept the same throughout the simulation.
`double.observer`	not currently implemented.
`region.obj`	an object of class Region created by a call to make.region
`design.obj`	an object of class Survey.Design created by a call to make.design
`population.description.obj`	an object of class Population.Description created by a call to make.population.description
`detectability.obj`	and object of class Detectabolity created by a call to make.detectability
`ddf.analyses.list`	a list of objects of class DDF.Analysis created by a call tomake.ddf.analysis.list

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.

object of class Simulation

Laura Marshall

## Not run: 
# A basic line transect simulation example
sim <- make.simulation()
check.sim.setup(sim)
sim <- run(sim) 
summary(sim)

# A basic point transect simulation example
sim <- make.simulation(design.obj = make.design("point"))
check.sim.setup(sim)
sim <- run(sim) 
summary(sim)
# Note % bias levels will vary due to low number of repetitions 
# set by default in these examples

# To increase the number of repetitions
sim <- make.simulation(reps = 100)
sim <- run(sim) 
summary(sim)

## End(Not run)

coords <- gaps <- list()
coords[[1]] <- list(data.frame(x = c(0,1000,1000,0,0), y = c(0,0,
 1000,1000,0)))
gaps[[1]] <- list(data.frame(x = c(400,600,500,350,400), y = c(100,
 250,600,120,100)))

region <- make.region(region.name = "study.area", units = "m", 
 coords = coords, gaps = gaps)
plot(region)

## Not run: 
data(transects.shp)
#Edit the pathway below to point to an empty folder where the
#transect shapefile will be saved
shapefile.pathway <- "C:/..."
write.shapefile(transects.shp, paste(shapefile.pathway,"/transects_1",
 sep = ""))

parallel.design <- make.design(transect.type = "Line", 
 design.details = c("Parallel","Systematic"), region = region, 
 design.axis = 0, spacing = 100, plus.sampling =FALSE, 
 path = shapefile.pathway)

pop.density <- make.density(region.obj = region, x.space = 10, 
 y.space = 10, constant = 0.5) 
pop.density <- add.hotspot(pop.density, centre = c(50, 200), 
 sigma = 100, amplitude = 0.1)
pop.density <- add.hotspot(pop.density, centre = c(500, 700), 
 sigma = 900, amplitude = 0.05)
pop.density <- add.hotspot(pop.density, centre = c(300, 100), 
 sigma = 100, amplitude = -0.15)

plot(pop.density)
plot(region, add = TRUE)

pop.description <- make.population.description(N = 1000, 
 density.obj = pop.density, region = region, fixed.N = TRUE)

detect <- make.detectability(key.function = "hn", scale.param = 15,
 truncation = 30) 

ddf.analyses <- make.ddf.analysis.list(dsmodel = list(~cds(key = "hn",
 formula = ~1),~cds(key = "hr", formula = ~1)), method = "ds", 
 criteria = "AIC")

my.simulation <- make.simulation(reps = 10, single.transect.set = TRUE,
 region.obj = region, design.obj = parallel.design, 
 population.description.obj = pop.description, 
 detectability.obj = detect, ddf.analyses.list = ddf.analyses)

survey.results <- create.survey.results(my.simulation, dht.table = TRUE)

plot(survey.results)

my.simulation <- run(my.simulation)

summary(my.simulation)

## End(Not run)