#' calculate_energy
#'
#' @description Calculate mean energy
#'
#' @param pattern List with reconstructed patterns.
#' @param weights Vector with weights used to calculate energy.
#' The first number refers to Gest(r), the second number to pcf(r).
#' @param return_mean Logical if the mean energy is returned.
#' @param verbose Logical if progress report is printed.
#'
#' @details
#' The function calculates the mean energy (or deviation) between the observed
#' pattern and all reconstructed patterns (for more information see Tscheschel &
#' Stoyan (2006) or Wiegand & Moloney (2014)). The pair correlation function and the
#' nearest neighbour distance function are used to describe the patterns.
#'
#' @seealso
#' \code{\link{plot_energy}} \cr
#' \code{\link{reconstruct_pattern}} \cr
#' \code{\link{fit_point_process}}
#'
#' @return vector
#'
#' @examples
#' pattern_random <- fit_point_process(species_a, n_random = 19)
#' calculate_energy(pattern_random)
#' calculate_energy(pattern_random, return_mean = TRUE)
#'
#' \dontrun{
#' marks_sub <- spatstat.geom::subset.ppp(species_a, select = dbh)
#' marks_recon <- reconstruct_pattern_marks(pattern_random$randomized[[1]], marks_sub,
#' n_random = 19, max_runs = 1000)
#' calculate_energy(marks_recon, return_mean = FALSE)
#' }
#'
#' @references
#' Kirkpatrick, S., Gelatt, C.D.Jr., Vecchi, M.P., 1983. Optimization by simulated
#' annealing. Science 220, 671–680. <https://doi.org/10.1126/science.220.4598.671>
#'
#' Tscheschel, A., Stoyan, D., 2006. Statistical reconstruction of random point
#' patterns. Computational Statistics and Data Analysis 51, 859–871.
#' <https://doi.org/10.1016/j.csda.2005.09.007>
#'
#' Wiegand, T., Moloney, K.A., 2014. Handbook of spatial point-pattern analysis in
#' ecology. Chapman and Hall/CRC Press, Boca Raton. ISBN 978-1-4200-8254-8
#'
#' @export
calculate_energy <- function(pattern,
weights = c(1, 1),
return_mean = FALSE,
verbose = TRUE){
# check if class is correct
if (!inherits(x = pattern, what = c("rd_pat", "rd_mar"))) {
stop("Class of 'pattern' must be 'rd_pat' or 'rd_mar'.",
call. = FALSE)
}
# check if observed pattern is present
if (!spatstat.geom::is.ppp(pattern$observed)) {
stop("Input must include 'observed' pattern.", call. = FALSE)
}
# extract observed pattern
pattern_observed <- pattern$observed
# extract randomized patterns
pattern_randomized <- pattern$randomized
# calculate r sequence
r <- seq(from = 0,
to = spatstat.explore::rmax.rule(W = pattern_observed$window,
lambda = spatstat.geom::intensity.ppp(pattern_observed)),
length.out = 250)
if (inherits(x = pattern, what = "rd_pat")) {
# get energy from df
if (is.list(pattern$energy_df)) {
result <- vapply(pattern$energy_df, FUN = function(x) utils::tail(x, n = 1)[[2]],
FUN.VALUE = numeric(1))
} else {
# calculate summary functions for observed pattern
gest_observed <- spatstat.explore::Gest(X = pattern_observed,
correction = "none", r = r)
pcf_observed <- spatstat.explore::pcf.ppp(X = pattern_observed, correction = "none", divisor = "d", r = r)
# loop through all reconstructed patterns
result <- vapply(seq_along(pattern_randomized), function(x) {
gest_reconstruction <- spatstat.explore::Gest(X = pattern_randomized[[x]],
correction = "none", r = r)
pcf_reconstruction <- spatstat.explore::pcf.ppp(X = pattern_randomized[[x]],
correction = "none", divisor = "d",
r = r)
# difference between observed and reconstructed pattern
energy <- (mean(abs(gest_observed[[3]] - gest_reconstruction[[3]]), na.rm = TRUE) * weights[[1]]) +
(mean(abs(pcf_observed[[3]] - pcf_reconstruction[[3]]), na.rm = TRUE) * weights[[2]])
# print progress
if (verbose) {
message("\r> Progress: ", x, "/", length(pattern_randomized), "\t\t",
appendLF = FALSE)
}
return(energy)
}, FUN.VALUE = numeric(1))
}
# set names
names(result) <- paste0("randomized_", seq_along(result))
} else if (inherits(x = pattern, what = "rd_mar")) {
# get energy from df
if (is.list(pattern$energy_df)) {
result <- vapply(pattern$energy_df, FUN = function(x) utils::tail(x, n = 1)[[2]],
FUN.VALUE = numeric(1))
} else {
# calculate summary functions
kmmr_observed <- spatstat.explore::markcorr(pattern_observed, correction = "Ripley",
r = r)
result <- vapply(seq_along(pattern_randomized), function(x) {
# calculate summary functions
kmmr_reconstruction <- spatstat.explore::markcorr(pattern_randomized[[x]],
correction = "Ripley",
r = r)
# difference between observed and reconstructed pattern
energy <- mean(abs(kmmr_observed[[3]] - kmmr_reconstruction[[3]]), na.rm = TRUE)
# print progress
if (verbose) {
message("\r> Progress: ", x, "/", length(pattern_randomized), "\t\t",
appendLF = FALSE)
}
return(energy)
}, FUN.VALUE = numeric(1))
}
# set names
names(result) <- paste0("randomized_", seq_along(result))
}
# return mean for all reconstructed patterns
if (return_mean) {
result <- mean(result)
}
# write result in new line if progress was printed
if (verbose) {
message("\r")
}
return(result)
}
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