rescale_local: Rescale trap coordinates, individual-level grid extent, and...

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

Rescale trap coordinates, individual-level grid extent, and starting activity center coordinates for local SCR approach

Description

Rescale inputs to prepare data for habitat mask to be used.

Usage

rescale_local(X, ext, ext_mat, s.st, site, hab_mask)

Arguments

X	An array representing the coordinates of traps in UTMs for each individual. This is returned from localize_classic.
ext	An Extent object from the raster package. This is returned from localize_classic.
ext_mat	A matrix of individual state-space grid extents returned from localize_classic.
s.st	A matrix of starting activity center coordinates. This is returned from localize_classic
site	Either NULL (if a 2D trap array is used) or a vector of integers denoting which trap array an individual (either detected or augmented) belongs to. Note that site is provided from sim_classic when a 3D trap array is used. However, this site variable must be correctly augmented based on the total augmented population size (i.e., M).
hab_mask	A matrix or arary output from mask_polygon or mask_raster functions.

Details

This function is only meant to be used when habitat masking is incorporated into a local SCR model. The functions properly rescales inputs for this SCR approach based onthe habitat mask. Note that the pixelWidth needs to be included as an input in the model after inputs are rescaled to correctly estimate the scaling parameter (i.e., 'sigma').

Value

• X Rescaled trap coordinates

• ext_mat Rescaled matrix of individual state-space grid extents.

• s.st A matrix of rescaled starting activity center coordinates.

Author(s)

Daniel Eacker

See Also

mask_polygon, mask_raster

Examples

## Not run: 
# simulate a single trap array with random positional noise
x <- seq(-1600, 1600, length.out = 6)
y <- seq(-1600, 1600, length.out = 6)
traps <- as.matrix(expand.grid(x = x, y = y))
# add some random noise to locations
set.seed(100)
traps <- traps + runif(prod(dim(traps)),-20,20) 
mysigma = 300 # simulate sigma of 300 m
mycrs = 32608 # EPSG for WGS 84 / UTM zone 8N
pixelWidth = 100 # grid resolution

# Simulated abundance
Nsim = 250

# Create initial grid and extent (use a slightly bigger buffer to match
# scaled-up state-space below)
Grid = grid_classic(X = traps, crs_ = mycrs, buff = 3.7*mysigma, res = pixelWidth)

# create polygon to use as a mask
library(sf)
poly = st_sfc(st_polygon(x=list(matrix(c(-2465,-2465,2530,-2550,2650,2550,
0,2550,-800,2500,-2350,2300,-2465,-2465),ncol=2, byrow=TRUE))), crs =  mycrs)

# make simple plot
par(mfrow=c(1,1))
plot(Grid$grid, pch=20)
points(traps, col="blue",pch=20)
plot(poly, add=TRUE)

# create habitat mask from polygon
hab_mask = mask_polygon(poly = poly, grid = Grid$grid, crs_ = mycrs, 
prev_mask = NULL)

data3d = sim_classic(X = traps, ext = Grid$ext, crs_ = mycrs, sigma_ = mysigma, 
                  prop_sex = 1, N = Nsim, K = 4, base_encounter = 0.15, 
                  enc_dist = "binomial", hab_mask = hab_mask, setSeed = 100)

# generate initial activity center coordinates for 2D trap array without 
# habitat mask
s.st = initialize_classic(y=data3d$y, M=500, X=traps, ext = Grid$ext, 
hab_mask = hab_mask)

# now use grid_classic to create an individual-level state-space (with origin 0, 0)
Grid_ind = grid_classic(X = matrix(c(0,0),nrow=1), crs_ = mycrs, 
                        buff = 3*mysigma, res = 100)

# now localize the data components created above 
local_list = localize_classic(y = data3d$y, grid_ind = Grid_ind$grid, X=traps, 
                            crs_ = mycrs, sigma_ = mysigma, s.st = s.st,
                            hab_mask = hab_mask)

# rescale inputs
rescale_list = rescale_local(X = local_list$X, ext = local_list$ext, 
                             ext_mat = local_list$ext_mat,
                             s.st = local_list$s.st, hab_mask = hab_mask)

# inspect rescaled list
str(rescale_list)

## End(Not run)

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