localize_classic | R Documentation |
A function to scale up from individual-level state-space to study-area level state-space using classic SCR data components.
localize_classic( y, grid_ind, X, crs_, sigma_, s.st, site = NULL, hab_mask = FALSE )
y |
Either a matrix or array of encounter history data, possibly
from |
grid_ind |
A matrix representing an individual state-space grid. This
is returned from |
X |
Either a matrix or array representing the coordinates of traps in UTMs. An array is used when traps are clustered over a survey area. |
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). |
sigma_ |
The scaling parameter of the bivariate normal kernel either
in meters or kilometers as an integer. Note that if |
s.st |
A matrix of starting activity center coordinates. This is
returned from |
site |
Either |
hab_mask |
Either |
This function converts classic SCR data to a format that is used in local evaluations of the individual state-space.
A list of data components needed to for classic SCR models in a local approach. Specifically, the function returns:
y
Individual-specific encounter history that only considers traps
within a distance of 9 times sigma_
.
X
Individual-specific trap array that only provides coordinates
for traps within a distance of 9 times sigma_
.
grid
A matrix or array of state-space grid coordinates that
encompasses all individual state-space grids.
prop_habitat
The proportion of suitable habitat for each individual.
Note that this is only returned when a habitat mask is used.
ext_mat
A matrix of individual state-space grid extents.
ext
An Extent
object from the raster
package for the
scaled-up state-space grid.
Jind
A vector with the number of traps each individual is exposed to.
s.st
A matrix of starting activity center coordinates.
Daniel Eacker
## 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 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 # 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) # Simulated abundance Nsim = 250 # simulate SCR data data3d = sim_classic(X = traps, ext = Grid$ext, crs_ = mycrs, sigma_ = mysigma, prop_sex = 1, N = Nsim, K = 4, base_encounter = 0.15, enc_dist = "binomial", hab_mask = FALSE, setSeed = 50) # generate initial activity center coordinates for 2D trap array without # habitat mask s.st = initialize_classic(y=data3d$y, M=500, X=traps, ext = Grid$ext, hab_mask = FALSE, all_random = FALSE) # now use grid_classic to create an individual-level state-space (with origin 0, 0) Grid_ind = grid_classic(X = matrix(c(0,0),nrow=1), crs_ = mycrs, buff = 3*mysigma, res = 100) # now localize the data components created above local_list = localize_classic(y = data3d$y, grid_ind = Grid_ind$grid, X=traps, crs_ = mycrs, sigma_ = mysigma, s.st = s.st, site = NULL, hab_mask = FALSE) # inspect local list str(local_list) ## End(Not run)
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