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#' SDMmodelCV
#'
#' This Class represents an SDMmodel model object with replicates and hosts all
#' the models trained during the cross validation.
#'
#' @slot models list. A list containing all the models trained during the cross
#' validation.
#' @slot data \linkS4class{SWD} object. Full dataset used to make the
#' partitions.
#' @slot folds list with two matrices, the first for the training and the second
#' for the testing dataset. Each column of one matrix represents a fold with
#' `TRUE` for the locations included in and `FALSE` excluded from the partition.
#'
#' @rdname SDMmodelCV-class
#' @include SWD-class.R
#' @export
#'
#' @author Sergio Vignali
SDMmodelCV <- setClass("SDMmodelCV",
representation(
models = "list",
data = "SWD",
folds = "list"
)
)
#' @param object SDMmodelCV object
#' @rdname SDMmodelCV-class
setMethod(
"show",
signature = "SDMmodelCV",
definition = function(object) {
tunable_hypers <- getTunableArgs(object@models[[1]])
cont_vars <- names(Filter(is.numeric, object@data@data))
if (length(cont_vars) == 0)
cont_vars <- NA
cat_vars <- names(Filter(is.factor, object@data@data))
if (length(cat_vars) == 0)
cat_vars <- NA
n_p <- sum(object@data@pa == 1)
n_a <- sum(object@data@pa == 0)
cli::cli_h2("Object of class: {.cls {class(object)}}")
cli::cli_par()
cli::cli_text("Method: {.emph { .get_method(object)}}")
cli::cli_end()
cli::cli_h3("Hyperparameters")
for (i in seq_along(tunable_hypers)) {
h <- slot(object@models[[i]]@model, tunable_hypers[i])
cli::cli_li("{.field {tunable_hypers[i]}}: {.val {h}}")
}
cli::cli_h3("Info")
cli::cli_li("{.field Species}: {.emph {object@data@species}}")
cli::cli_li("{.field Replicates}: {.val {length(object@models)}}")
cli::cli_li("{.field Total presence locations}: {.val {n_p}}")
cli::cli_li("{.field Total absence locations}: {.val {n_a}}")
cli::cli_h3("Variables")
cli::cli_li("{.field Continuous}: {.val {cont_vars}}")
cli::cli_li("{.field Categorical}: {.val {cat_vars}}")
}
)
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