Creates a conditional empirical random walk, with a specific starting and ending point, geometrically similar to the initial trajectory (extractMethod: raster overlay method can take "simple" or "bilinear")
sim.cond.3d(
n.locs,
start = c(0, 0, 0),
end = start,
a0,
g0,
densities,
qProbs,
error = FALSE,
DEM = NULL,
BG = NULL
)length of the trajectory in locations
numeric vector of length 3 with the coordinates of the start point
numeric vector of length 3 with the coordinates of the end point
initial incoming heading in radian
initial incoming gradient/polar angle in radian
list object returned by the get.densities.3d function
list object returned by the qProb.3d function
logical: add random noise to the turn angle, lift angle and step length to account for errors measurements?
raster layer containing a digital elevation model, covering the area between start and end point
a background raster layer that can be used to inform the choice of steps
A trajectory in the form of data.frame
niclas <- track.properties.3d(niclas)
n.locs <- 3
P <- get.track.densities.3d(niclas)
#> |TLD cube dimensions: 9 x 14 x 3
f <- 1500
start <- Reduce(c, niclas[1, 1:3])
end <- Reduce(c, niclas[n.locs, 1:3])
a0 <- niclas$a[1]
g0 <- niclas$g[1]
uerw <- sim.uncond.3d(
n.locs * f, start = start, a0 = a0, g0 = g0, densities = P
)
#> |Simulate UERW with 4500 steps
#>
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#> |Elapsed time: 0.3s
Q <- qProb.3d(uerw, n.locs)
#> |Extracting Q probabilities for 3 steps
#>
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#> |Elapsed time: 0s
sim.cond.3d(
n.locs = n.locs, start = start, end = end,
a0 = a0, g0 = g0, densities = P, qProbs = Q
)
#> |Simulate CERW with 3 steps
#>
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#> |Elapsed time: 0s
#> x y z a g t l d
#> 1 2556476 1188336 1283.736 0.3914766 1.557032 0.23629734 -0.03443615 NA
#> 2 2558851 1189402 1350.465 0.4220587 1.545164 0.03058205 -0.01186791 2603.554
#> 3 2560477 1189861 1369.713 NA NA NA NA 1689.868
#> p
#> 1 NA
#> 2 1.865802e-17
#> 3 NA