#' @include Algorithm.SDM.R
#' @importFrom raster raster stack
NULL
#'An S4 class to represent an ensemble SDM
#'
#'This is an S4 class to represent an ensemble SDM from multiple algorithms
#'(including generalized linear model, general additive model, multivariate
#'adaptive splines, generalized boosted regression model, classification tree
#'analysis, random forest, maximum entropy, artificial neural network, and
#'support vector machines). This S4 class is returned by
#'\code{\link{ensemble_modelling}} or \code{\link{ensemble}}.
#'
#'@slot uncertainty raster. Between-algorithm variance map.
#'@slot algorithm.correlation data frame. Between-algorithm correlation matrix.
#'@slot algorithm.evaluation data frame. Evaluation of the ensemble SDM (available
#'@slot sdms list. Individual SDMs used to create the ESDM.
#' metrics include AUC, Kappa, sensitivity, specificity and proportion of
#' correctly predicted occurrences) and identification of the optimal threshold
#' to convert the habitat suitability map into a binary presence/absence map.
#'
#'@seealso \linkS4class{Algorithm.SDM} an S4 class to represent an SDM based on
#' a single algorithm, and \linkS4class{Stacked.SDM} an S4 class for SSDMs.
#'
#'@export
setClass('Ensemble.SDM',
contains = 'SDM',
representation(uncertainty = 'Raster',
algorithm.correlation = 'data.frame',
algorithm.evaluation = 'data.frame',
sdms = 'list'),
prototype(uncertainty = raster(),
algorithm.correlation = data.frame(),
algorithm.evaluation = data.frame(),
sdms = list()))
Ensemble.SDM <- function(name = character(),
projection = raster(),
binary = raster(),
evaluation = data.frame(),
variable.importance = data.frame(),
data = data.frame(),
uncertainty = raster(),
algorithm.correlation = data.frame(),
algorithm.evaluation = data.frame(),
parameters = data.frame(matrix(nrow = 1, ncol = 0)),
sdms = list()) {
return(new('Ensemble.SDM',
name = name,
projection = projection,
binary = binary,
evaluation = evaluation,
variable.importance = variable.importance,
data = data,
uncertainty = uncertainty,
algorithm.correlation = algorithm.correlation,
algorithm.evaluation = algorithm.evaluation,
parameters = parameters,
sdms = sdms))}
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