Simulates n tracks with the geometrical properties of the original track, between the same start and end point.
reproduce.track.3d(
track,
n.sim = 1,
parallel = FALSE,
error = TRUE,
DEM = NULL,
BG = NULL,
filterDeadEnds = TRUE,
plot2d = FALSE,
plot3d = FALSE,
maxBin = 25,
gradientDensity = TRUE
)data.frame with x,y,z coordinates of the original track
number of simulations that should be done
logical: run computations in parallel (n-1 cores)? Or numeric: the number of nodes (maximum: n - 1 cores)
logical: add error term to movement in simulation?
a raster containing a digital elevation model, covering the same extent as the track
a raster influencing the probabilities.
logical: Remove tracks that ended in a dead end?
logical: plot tracks on 2-D plane?
logical: plot tracks in 3-D?
numeric scalar, maximum number of bins per dimension of the tld-cube (turnLiftStepHist)
logical: Should a distribution of the gradient angle be extracted and used in the simulations (get.densities.3d)?
A list or data.frame containing the simulated track(s) (CERW).
reproduce.track.3d(niclas[1:10, ])
#> |TLD cube dimensions: 8 x 15 x 2
#> |Simulate UERW with 15000 steps
#>
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#> |Elapsed time: 0.9s
#> |Extracting Q probabilities for 10 steps
#>
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#> |Elapsed time: 0.2s
#> |Simulate CERW with 10 steps
#>
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#> |Elapsed time: 0.2s
#> x y z a g t l d
#> 1 2556476 1188336 1283.736 0.4477398 1.531790 0.09509566 0.01092540 NA
#> 2 2561070 1190971 1304.635 0.5207478 1.566850 0.05846231 0.03405443 5306.855
#> 3 2565962 1193643 1393.923 0.4998713 1.554781 -0.01480439 -0.01220363 5575.861
#> 4 2568251 1194526 1401.960 0.3685516 1.567520 -0.12470443 0.01092540 2371.274
#> 5 2571994 1195924 1307.642 0.3573387 1.594398 -0.01480439 0.03405443 3972.500
#> 6 2573435 1196403 1268.271 0.3208064 1.596720 -0.05143774 0.01092540 1568.526
#> 7 2575379 1197112 1231.491 0.3496806 1.588568 0.02182896 -0.01220363 2104.403
#> 8 2577648 1198269 1180.465 0.4714894 1.590825 0.13172901 0.01092540 2638.145
#> 9 2579396 1199464 1110.940 0.5999363 1.603623 0.13172901 0.01092540 2104.403
#> 10 2581749 1201865 1513.318 NA NA NA NA 3385.763
#> p
#> 1 NA
#> 2 7.019249e-19
#> 3 1.403985e-17
#> 4 3.498323e-18
#> 5 1.052505e-18
#> 6 3.920724e-18
#> 7 5.434983e-19
#> 8 1.282004e-18
#> 9 2.222237e-18
#> 10 NA