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#' Get Tunable Arguments
#'
#' Returns the name of all function arguments that can be tuned for a given
#' model.
#'
#' @param model \linkS4class{SDMmodel} or \linkS4class{SDMmodelCV} object.
#'
#' @return character vector.
#' @export
#'
#' @author Sergio Vignali
#'
#' @examples
#' # Acquire environmental variables
#' files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
#' pattern = "grd", full.names = TRUE)
#' predictors <- raster::stack(files)
#'
#' # Prepare presence and background locations
#' p_coords <- virtualSp$presence
#' bg_coords <- virtualSp$background
#'
#' # Create SWD object
#' data <- prepareSWD(species = "Virtual species", p = p_coords, a = bg_coords,
#' env = predictors, categorical = "biome")
#'
#' # Train a Maxnet model and get tunable hyperparameters
#' model <- train(method = "Maxnet", data = data, fc = "l")
#' getTunableArgs(model)
getTunableArgs <- function(model) {
if (class(model) == "SDMmodelCV") {
method <- class(model@models[[1]]@model)
} else {
method <- class(model@model)
}
if (method == "Maxent") {
args <- c("fc", "reg", "iter")
} else if (method == "Maxnet") {
args <- c("fc", "reg")
} else if (method == "ANN") {
args <- c("size", "decay", "rang", "maxit")
} else if (method == "RF") (
args <- c("mtry", "ntree", "nodesize")
) else {
args <- c("distribution", "n.trees", "interaction.depth", "shrinkage",
"bag.fraction")
}
return(args)
}
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