make.detectability: Creates a Detectablility object

In DSsim: Distance Sampling Simulations

Description

The detectability of the population is described by the values in this class.

Usage

make.detectability(
  key.function = "hn",
  scale.param = 25,
  shape.param = numeric(0),
  cov.param = list(),
  truncation = 50
)

Arguments

key.function	specifies shape of the detection function (either half-normal "hn", hazard rate "hr" or uniform "uf")
scale.param	numeric vector with either a single value to be applied globally or a value for each strata. These should be supplied on the natural scale.
shape.param	numeric vector with either a single value to be applied globally or a value for each strata. These should be supplied on the natural scale.
cov.param	Named list with one named entry per individual level covariate. Covariate parameter values should be defined on the log scale (rather than the natural scale), this is the same scale as provided in the ddf output in mrds and also in the MCDS output in Distance. Cluster sizes parameter values can be defined here. Each list entry will either be a data.frame containing 2 or 3 columns: level, param and where desired strata. If the region has multiple strata but this column is omitted then the values will be assumed to apply globally. The cluster size entry in the list must be named 'size'. Alternatively the list element may a numeric vector with either a single value to be applied globally or a value for each strata.
truncation	the maximum perpendicular (or radial) distance at which objects may be detected from a line (or point) transect.

Value

object of class Detectablility

Author(s)

Laura Marshall

Examples

# The default values create a detectability object with a half normal
# detection function with scale parameter 25 and truncation distance 50.
detect <- make.detectability() 
detect

# To include covariate parameters which affect detecability,
# first you need to make sure the population has covariates defined 
# see examples in ?make.population.description
# Multi-strata covariate example
# Make a multi strata region
poly1 <- data.frame(x = c(0,0,100,100,0), y = c(0,100,100,0,0))
poly2 <- data.frame(x = c(200,200,300,300,200), y = c(10,110,110,10,10))
coords <- list(list(poly1), list(poly2))
region <- make.region(coords = coords)
density <- make.density(region)
# Create the population description
covariate.list <- list()
covariate.list$size <- list(list("ztruncpois", list(mean = 3)),
                            list("ztruncpois", list(mean = 5)))
covariate.list$height <- list(list("lognormal", list(meanlog = log(2), sdlog = log(1.25))))
covariate.list$sex <- list(data.frame(level = c("male", "female"), prob = c(0.45,0.55)), 
                           data.frame(level = c("male", "female"), prob = c(0.5,0.5)))
pop.desc <- make.population.description(region.obj = region, 
                                        density.obj = density, 
                                        covariates = covariate.list, 
                                        N = c(10,10))

# In this example height and sex have a global effect where as the effects of size on 
# detectability vary by strata.
cov.params <- list(size = c(log(1.05), log(1.1)), 
                   height = log(1.2), 
                   sex = data.frame(level = c("male", "female"), 
                                    param = c(log(1), log(0.6))))

detect <- make.detectability(key.function = "hn", scale.param = 20, 
                             truncation = 50, cov.param = cov.params)

plot(detect, pop.desc)
                                    
# If we want the effects of sex to be strata specific we can define detectability as follows:
cov.params <- list(size = c(0.05, 0.1), 
                   height = 0.2, 
                   sex = data.frame(level = c("male", "female","male", "female"), 
                                    strata = c("A", "A", "B", "B"),
                                    param = c(0,-0.5, 0.1, -0.25)))  
                                                                       
detect <- make.detectability(key.function = "hn", scale.param = c(20, 25), 
                             truncation = 60, cov.param = cov.params)
plot(detect, pop.desc)

DSsim documentation built on March 26, 2020, 7:39 p.m.