sim_classic: Simulate basic spatial capture-recapture data

In sitkensis22/localSCR: Supporting functions for advanced Bayesian spatial capture-recapture modeling using 'nimble'

Simulate basic spatial capture-recapture data

Description

Returns a list of simulated data including the encounter history, binary sex indicator, activity centers, and site identifier.

Usage

sim_classic(
  X,
  ext,
  crs_,
  N,
  sigma_,
  prop_sex,
  K,
  base_encounter,
  enc_dist = "binomial",
  hab_mask = FALSE,
  setSeed = 500
)

Arguments

X	Either a matrix or array object representing the coordinates of traps in UTMs. An array is used when traps are clustered over a survey area.
ext	An Extent object from the raster package. This is returned from grid_classic.
crs_	The UTM coordinate reference system (EPSG code) used for your location provided as an integer (e.g., 32608 for WGS 84/UTM Zone 8N).
N	Simulated total abundance as an integer.
sigma_	The scaling parameter of the bivariate normal kernel either in meters or kilometers as an integer.
prop_sex	The portion of females or males as a numeric value. This will depend upon the indicator coding scheme used (e.g., females = 1 and males = 0; then proportion of females in the simulation). Must be a numeric value between 0 and 1. Note that 0 or 1 can be used if a non-sex-specific sigma is desired.
K	The number of sampling occasions desired as an integer.
base_encounter	The baseline encounter probability or rate as a numeric value. Note that a probabilty is given for a "binomial" observation distribution while a rate is given for a "poisson" distribution.
enc_dist	Either "binomial" or "poisson". Default is "binomial".
hab_mask	Either FALSE (the default) or a matrix or arrary output from mask_polygon or mask_raster functions.
setSeed	The random number generater seed as an integer used in simulations to obtain repeatable data simulations. Default is 500.

Details

This function supports spatial capture-recapture (SCR) analysis by allowing for easy simulation of data components used by nimble in Baysian SCR models. Note that the output for the encounter histories y will be sorted by detected and not detected individuals.

Value

• y A list of a matrix or array of encounter histories.

• sex A vector or matrix of 0's and 1's for sex identification.

• s A matrix of simulated activity centers.

• site A vector for the site identifier.

Note

The site identfier is only returned when a 3-dimensional trap array is provided.

Author(s)

Daniel Eacker

See Also

grid_classic

Examples

# simulate a single trap array with random positional noise
x <- seq(-800, 800, length.out = 5)
y <- seq(-800, 800, length.out = 5)
traps <- as.matrix(expand.grid(x = x, y = y))

# add some random noise to locations
traps <- traps + runif(prod(dim(traps)),-20,20) 

mysigma = 300 # simulate sigma of 300 m
mycrs = 32608 # EPSG for WGS 84 / UTM zone 8N

# Create grid and extent
Grid = grid_classic(X = traps, crs_ = mycrs, buff = 3*mysigma, res = 100)

# simulate SCR data
data3d = sim_classic(X = traps, ext = Grid$ext, crs_ = mycrs, sigma_ = c(300), 
prop_sex = 1, N = 200, K = 4, base_encounter = 0.25, enc_dist = "binomial", 
hab_mask = FALSE, setSeed = 50)

# make simple plot
par(mfrow=c(1,1))
plot(Grid$grid, pch=20,ylab="Northing",xlab="Easting")
points(traps, col="blue",pch=20)
points(data3d$s,col="red",pch = 20)
points(data3d$s[which(apply(data3d$y,1,sum)!=0),],col="green",pch = 20)

sitkensis22/localSCR documentation built on May 15, 2022, 5:26 p.m.