sim.cond.3d: Conditional Empirical Random Walk (CERW) in 3-D

In munterfinger/eRTG3D: Empirically Informed Random Trajectory Generation in 3-D

Conditional Empirical Random Walk (CERW) in 3-D

Description

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")

Usage

sim.cond.3d(
  n.locs,
  start = c(0, 0, 0),
  end = start,
  a0,
  g0,
  densities,
  qProbs,
  error = FALSE,
  DEM = NULL,
  BG = NULL
)

Arguments

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?
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

Value

A trajectory in the form of data.frame

Examples

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)
sim.cond.3d(
  n.locs = n.locs, start = start, end = end,
  a0 = a0, g0 = g0, densities = P, qProbs = Q
)

munterfinger/eRTG3D documentation built on March 25, 2022, 1:22 a.m.