reconstruct_pattern_multi: reconstruct_pattern_multi

In mhesselbarth/SHAR: Species-Habitat Associations

reconstruct_pattern_multi

Description

Pattern reconstruction of a pattern marked by multiple traits.

Usage

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 = 1e-04,
  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
)

Arguments

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. 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	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 spatstat package to be included in the energy calculation. This may include Dk, K, Hs, pcf.
verbose	Logical if progress report is printed.

Details

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: X-coordinate, Y-coordinate, 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).

Value

rd_multi

References

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 point-pattern analysis in ecology. Chapman and Hall/CRC Press, Boca Raton. ISBN 978-1-4200-8254-8

Wudel, C., Schlicht, R., & Berger, U. (2023). Multi-trait point pattern reconstruction of plant ecosystems. Methods in Ecology and Evolution, 14, 2668–2679. https://doi.org/10.1111/2041-210X.14206

See Also

fit_point_process
reconstruct_pattern
reconstruct_pattern_marks

Examples

## 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)

mhesselbarth/SHAR documentation built on March 19, 2024, 12:24 p.m.