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#' RELMUTINF (landscape level)
#'
#' @description Relative mutual information
#'
#' @param landscape A categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars or a list of SpatRasters.
#' @param neighbourhood The number of directions in which cell adjacencies are considered as neighbours:
#' 4 (rook's case) or 8 (queen's case). The default is 4.
#' @param ordered The type of pairs considered.
#' Either ordered (TRUE) or unordered (FALSE).
#' The default is TRUE.
#' @param base The unit in which entropy is measured.
#' The default is "log2", which compute entropy in "bits".
#' "log" and "log10" can be also used.
#'
#' @details
#' Due to the spatial autocorrelation, the value of mutual information tends to grow
#' with a diversity of the landscape (marginal entropy). To adjust this tendency,
#' it is possible to calculate relative mutual information by dividing the mutual
#' information by the marginal entropy. Relative mutual information always has a
#' range between 0 and 1 and can be used to compare spatial data with different
#' number and distribution of categories. When the value of mutual information equals
#' to 0, then relative mutual information is 1.
#'
#' @seealso
#' \code{\link{lsm_l_ent}},
#' \code{\link{lsm_l_condent}},
#' \code{\link{lsm_l_joinent}},
#' \code{\link{lsm_l_mutinf}}
#'
#' @return tibble
#'
#' @examples
#' landscape <- terra::rast(landscapemetrics::landscape)
#' lsm_l_relmutinf(landscape)
#'
#' @references
#' Nowosad J., TF Stepinski. 2019. Information theory as a consistent framework
#' for quantification and classification of landscape patterns. https://doi.org/10.1007/s10980-019-00830-x
#'
#' @export
lsm_l_relmutinf <- function(landscape,
neighbourhood = 4,
ordered = TRUE,
base = "log2") {
landscape <- landscape_as_list(landscape)
result <- lapply(X = landscape,
FUN = lsm_l_relmutinf_calc,
neighbourhood = neighbourhood,
ordered = ordered,
base = base)
layer <- rep(seq_along(result),
vapply(result, nrow, FUN.VALUE = integer(1)))
result <- do.call(rbind, result)
tibble::add_column(result, layer, .before = TRUE)
}
lsm_l_relmutinf_calc <- function(landscape, neighbourhood, ordered, base, extras = NULL){
# convert to matrix
if (!inherits(x = landscape, what = "matrix")) {
landscape <- terra::as.matrix(landscape, wide = TRUE)
}
# all values NA
if (all(is.na(landscape))) {
return(tibble::new_tibble(list(level = "landscape",
class = as.integer(NA),
id = as.integer(NA),
metric = "mutinf",
value = as.double(NA))))
}
if (!is.null(extras)){
comp <- extras$comp
cplx <- extras$cplx
} else {
com <- rcpp_get_coocurrence_matrix(landscape, directions = as.matrix(neighbourhood))
comp <- rcpp_get_entropy(colSums(com), base)
cplx <- get_complexity(landscape, neighbourhood, ordered, base)
}
conf <- cplx - comp
aggr <- comp - conf
rel <- ifelse(aggr == 0, 1, aggr / comp)
return(tibble::new_tibble(list(level = rep("landscape", length(rel)),
class = rep(as.integer(NA), length(rel)),
id = rep(as.integer(NA), length(rel)),
metric = rep("relmutinf", length(rel)),
value = as.double(rel))))
}
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