This simulates line transect distance sampling data with a spatial distribution of objects in a heterogeneous landscape where the parameter beta controls the effect of habitat. Habitat is simulated according to a Gaussian random field model defined within the function. Uses a half normal detection model (if perp = TRUE) or a Gaussian hazard model (perp = FALSE).
To recreate the data sets used in the book with R 3.6.0 or later, include sample.kind="Rounding" in the call to set.seed. This should only be used for reproduction of old results.
simSpatialDSline(N=1000, beta = 1, sigma=0.25, alpha0 = -2, W=1/2, L = 4,
perp=FALSE, show.plots=TRUE)total population size in the rectangle
coefficient of spatial covariate x for the density model.
scale of half-normal detection function
intercept of the hazard function.
half-width of the rectangle, which extends W each side of the transect line.
length of the transect.
if TRUE, data are simulated for a traditional distance sampling model with perpendicular distances; if FALSE (the default) a model with 'forward distance' data, ie, the distance from the observer to the animal on first detection.
if TRUE, summary plots are displayed.
A list with the values of the input arguments and the following additional elements:
the distance between pixel centers (spatial resolution of the raster
2-column matrix with x/y coordinates of all pixels
value of habitat covariate for each pixel
a Raster object with the habitat covariate
x and y coordinates for all the animals in the population
2-column matrix of trap locations
a 2-column matrix with x and y coordinates of each animal captured.
pixel ID for each animal captured.
a matrix with rows for each animal captured and columns for trap of first capture, distance from trap to animal, and x and y coordinates of the animal.
probability that each animal is the population is captured at least once
pixel ID for each animal captured.
K<U+00E9>ry, M. & Royle, J.A. (2021) Applied Hierarchical Modeling in Ecology AHM2 - 11.
# NOT RUN {
# Run the function with default values and look at the output
str(tmp <- simSpatialDSline(), 1) # use str(., max.level=1) to limit the amount of output.
# }
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