View source: R/reconstruct_pattern_multi.R
reconstruct_pattern_multi  R Documentation 
Pattern reconstruction of a pattern marked by multiple traits.
reconstruct_pattern_multi(
marked_pattern,
xr = marked_pattern$window$xrange,
yr = marked_pattern$window$yrange,
n_repetitions = 1,
max_steps = 10000,
no_change = 5,
rcount = 250,
rmax = 25,
issue = 1000,
divisor = "r",
kernel_arg = "epanechnikov",
timing = FALSE,
energy_evaluation = FALSE,
plot = FALSE,
Lp = 1,
bw = if (divisor %in% c("r", "d")) 0.5 else 5,
sd = "step",
steps_tol = 1000,
tol = 1e04,
w_markcorr = c(d_d = 1, all = 1, d_all = 1, all_all = 1, d_d0 = 1, all0 = 1, d_all0 =
1, all_all0 = 1),
prob_of_actions = c(move_coordinate = 0.4, switch_coords = 0.1, exchange_mark_one =
0.1, exchange_mark_two = 0.1, pick_mark_one = 0.2, pick_mark_two = 0.1),
k = 1,
w_statistics = c(),
verbose = TRUE
)
marked_pattern 
ppp object with marked pattern. See Details section for more information. 
xr , yr 
Maximum extent in x and y direction of observation window. 
n_repetitions 
Integer representing the number of simulations to be performed. 
max_steps 
Maximum number simulation steps. 
no_change 
Integer representing the number of iterations (per 1000 simulation steps) after which the reconstruction is terminated if the energy does not decrease. 
rcount 
Integer representing the number of intervals for which the summary statistics are evaluated. 
rmax 
Maximum distance [m] up to which the summary statistics are evaluated. 
issue 
Integer that determines after how many simulations steps an output occurs. 
divisor 
Choice of divisor in the estimation formula: either "r" or "d". 
kernel_arg 
The kernel used to calculate the energy, possible kernels can be: Gaussian, Epanechnikov, Rectangular, Cumulative. 
timing 
Logical value: The computation time is measured if this is TRUE. 
energy_evaluation 
Logical value: If this is TRUE, the procedure stores the energy shares of the total energy per simulation step. 
plot 
Logical value: If this is TRUE, the procedure records the point pattern during optimization and updated. 
Lp 
Distance measure for the calculation of the energy function (Lp distance, 1 <= p < Inf). 
bw 
Bandwidth [m] with which the kernels are scaled, so that this is the standard deviation of the smoothing kernel. 
sd 
This is the standard deviation [m] used in the move_coordinate action. 
steps_tol 
After the value steps_tol it is checked whether the energy change is smaller than tol. 
tol 
Stops the procedure of energy if more than 1  tol times no changes. 
w_markcorr 
Vector of possible weightings of individual mcf's. (Default: all equal). 
prob_of_actions 
Vector of probabilities for the actions performed.

k 
Vector of values k; used only if Dk is included in w_statistics. 
w_statistics 
vector of named weights for optional spatial statistics
from the 
verbose 
Logical if progress report is printed. 
A novel approach carries out a pattern reconstruction of marked dot patterns as described by Tscheschel and Stoyan (2006) and Wiegand and Moloney (2014).
One particular feature is the simultaneous consideration of both marks, accounting for their correlation during reconstruction.
The marked point pattern (PPP object) must is currently structured as follows: Xcoordinate, Ycoordinate, metric mark (e.g. diameter at breast height), and nominal mark (e.g. tree species).It is calculated in the unit metre [m].
A combination of the mark correlation function and pair correlation function is used for pattern description. Additional summary statistics may be considered.Two randomly selected marks are chosen in each iteration, and one of various actions is performed. Changes will only be retained if the difference between the observed and reconstructed pattern decreases (minimizing energy).
This method is currently only suitable for homogeneous point patterns.
A comprehensive description of the method can be found in Wudel et al. (2023).
rd_multi
Kirkpatrick, S., Gelatt, C.D.Jr., Vecchi, M.P., 1983. Optimization by simulated annealing. Science 220, 671–680. <https://doi.org/10.1126/science.220.4598.671>
Tscheschel, A., Stoyan, D., 2006. Statistical reconstruction of random point patterns. Computational Statistics and Data Analysis 51, 859–871. <https://doi.org/10.1016/j.csda.2005.09.007>
Wiegand, T., Moloney, K.A., 2014. Handbook of spatial pointpattern analysis in ecology. Chapman and Hall/CRC Press, Boca Raton. ISBN 9781420082548
Wudel, C., Schlicht, R., & Berger, U. (2023). Multitrait point pattern reconstruction of plant ecosystems. Methods in Ecology and Evolution, 14, 2668–2679. https://doi.org/10.1111/2041210X.14206
fit_point_process
reconstruct_pattern
reconstruct_pattern_marks
## Not run:
# Random example data set
xr < 500
yr < 1000
N < 400
y < runif(N, min = 0, max = yr)
x < runif(N, min = 0, max = xr)
species < sample(c("A","B"), N, replace = TRUE)
diameter < runif(N, 0.1, 0.4)
random < data.frame(x = x, y = y, dbh = diameter, species = factor(species))
marked_pattern < spatstat.geom::as.ppp(random, W = spatstat.geom::owin(c(0, xr), c(0, yr)))
# Reconstruction function
reconstruction < reconstruct_pattern_multi(marked_pattern, n_repetitions = 2,
max_steps = 10000)
## End(Not run)
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