n.sim.cond.3d | R Documentation |
Creates multiple conditional empirical random walks, with a specific starting and ending point, geometrically similar to the initial trajectory by applying sim.cond.3d multiple times.
n.sim.cond.3d( n.sim, n.locs, start = c(0, 0, 0), end = start, a0, g0, densities, qProbs, error = FALSE, parallel = FALSE, DEM = NULL, BG = NULL )
n.sim |
number of CERWs to simulate |
n.locs |
length of the trajectory in locations |
start |
numeric vector of length 3 with the coordinates of the start point |
end |
numeric vector of length 3 with the coordinates of the end point |
a0 |
initial incoming heading in radian |
g0 |
initial incoming gradient/polar angle in radian |
densities |
list object returned by the get.densities.3d function |
qProbs |
list object returned by the qProb.3d function |
error |
logical: add random noise to the turn angle, lift angle and step length to account for errors measurements? |
parallel |
logical: run computations in parallel (n-1 cores)? Or numeric: the number of nodes (maximum: n - 1 cores) |
DEM |
raster layer containing a digital elevation model, covering the area between start and end point |
BG |
a background raster layer that can be used to inform the choice of steps |
A list containing the CERWs or NULL
s if dead ends have been encountered.
niclas <- track.properties.3d(niclas) n.locs <- 3 P <- get.track.densities.3d(niclas) 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 ) Q <- qProb.3d(uerw, n.locs) n.sim.cond.3d( n.sim = 2, n.locs = n.locs, start = start, end = end, a0 = a0, g0 = g0, densities = P, qProbs = Q )
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