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#' Expected value of the decision given survey information
#'
#' Calculate the *expected value of the management decision
#' given survey information*. This metric describes the value of the management
#' decision that is expected when the decision maker surveys a
#' set of sites to help inform the decision.
#'
#' @inheritParams fit_xgb_occupancy_models
#'
#' @param site_probability_columns `character` names of `numeric`
#' columns in the argument to `site_data` that contain modeled
#' probabilities of occupancy for each feature in each site.
#' Each column should correspond to a different feature, and contain
#' probability data (values between zero and one). No missing (`NA`)
#' values are permitted in these columns.
#'
#' @param site_survey_scheme_column `character` name of `logical`
#' (`TRUE` / `FALSE`) column in the argument to `site_data`
#' that indicates which sites are selected in the scheme or not.
#' No missing `NA` values are permitted. Additionally, only sites
#' that are missing data can be selected or surveying (as per the
#' argument to `site_detection_columns`).
#'
#' @param feature_survey_column `character` name of the column in the
#' argument to `feature_data` that contains `logical` (`TRUE` /
#' `FALSE`) values indicating if the feature will be surveyed in
#' the planned surveys or not. Note that considering additional features will
#' rapidly increase computational burden, and so it is only recommended to
#' consider features that are of specific conservation interest.
#' No missing (`NA`) values are permitted in this column.
#'
#' @param site_survey_cost_column `character` name of column in the
#' argument to `site_data` that contains costs for surveying each
#' site. This column should have `numeric` values that are equal to
#' or greater than zero. No missing (`NA`) values are permitted in this
#' column.
#'
#' @param site_management_cost_column `character` name of column in the
#' argument to `site_data` that contains costs for managing each
#' site for conservation. This column should have `numeric` values that
#' are equal to or greater than zero. No missing (`NA`) values are
#' permitted in this column.
#'
#' @param feature_model_sensitivity_column `character` name of the
#' column in the argument to `feature_data` that contains
#' probability of the initial models correctly predicting a presence of each
#' feature in a given site (i.e. the sensitivity of the models).
#' This column should have `numeric` values that are between zero and
#' one. No missing (`NA`) values are permitted in this column.
#' This should ideally be calculated using
#' [fit_xgb_occupancy_models()] or
#' [fit_hglm_occupancy_models()].
#'
#' @param feature_model_specificity_column `character` name of the
#' column in the argument to `feature_data` that contains
#' probability of the initial models correctly predicting an absence of each
#' feature in a given site (i.e. the specificity of the models).
#' This column should have `numeric` values that are between zero and
#' one. No missing (`NA`) values are permitted in this column.
#' This should ideally be calculated using
#' [fit_xgb_occupancy_models()] or
#' [fit_hglm_occupancy_models()].
#'
#' @param feature_target_column `character` name of the column in the
#' argument to `feature_data` that contains the \eqn{target}
#' values used to parametrize the conservation benefit of managing of each
#' feature.
#' This column should have `numeric` values that
#' are equal to or greater than zero. No missing (`NA`) values are
#' permitted in this column.
#'
#' @param total_budget `numeric` maximum expenditure permitted
#' for conducting surveys and managing sites for conservation.
#'
#' @param site_management_locked_in_column `character` name of the column
#' in the argument to `site_data` that contains `logical`
#' (`TRUE` / `FALSE`) values indicating which sites should
#' be locked in for (`TRUE`) being managed for conservation or
#' (`FALSE`) not. No missing (`NA`) values are permitted in this
#' column. This is useful if some sites have already been earmarked for
#' conservation, or if some sites are already being managed for conservation.
#' Defaults to `NULL` such that no sites are locked in.
#'
#' @param site_management_locked_out_column `character` name of the column
#' in the argument to `site_data` that contains `logical`
#' (`TRUE` / `FALSE`) values indicating which sites should
#' be locked out for (`TRUE`) being managed for conservation or
#' (`FALSE`) not. No missing (`NA`) values are permitted in this
#' column. This is useful if some sites could potentially be surveyed
#' to improve model predictions even if they cannot be managed for
#' conservation. Defaults to `NULL` such that no sites are locked out.
#'
#' @param prior_matrix `numeric` `matrix` containing
#' the prior probability of each feature occupying each site.
#' Rows correspond to features, and columns correspond to sites.
#' Defaults to `NULL` such that prior data is calculated automatically
#' using [prior_probability_matrix()].
#'
#' @details This function calculates the expected value and does not
#' use approximation methods. As such, this function can only be applied
#' to very small problems.
#'
#' @return A `numeric` value.
#'
#' @seealso [prior_probability_matrix()].
#'
#' @examples
#' # set seeds for reproducibility
#' set.seed(123)
#'
#' # load example site data
#' data(sim_sites)
#' print(sim_sites)
#'
#' # load example feature data
#' data(sim_features)
#' print(sim_features)
#'
#' # set total budget for managing sites for conservation
#' # (i.e. 50% of the cost of managing all sites)
#' total_budget <- sum(sim_sites$management_cost) * 0.5
#'
#' # create a survey scheme that samples the first two sites that
#' # are missing data
#' sim_sites$survey_site <- FALSE
#' sim_sites$survey_site[which(sim_sites$n1 < 0.5)[1:2]] <- TRUE
#'
#' # calculate expected value of management decision given the survey
#' # information using exact method
#' ev_survey <- evdsi(
#' sim_sites, sim_features,
#' c("f1", "f2", "f3"), c("n1", "n2", "n3"), c("p1", "p2", "p3"),
#' "management_cost", "survey_site",
#' "survey_cost", "survey", "survey_sensitivity", "survey_specificity",
#' "model_sensitivity", "model_specificity",
#' "target", total_budget)
#'
#' # print value
#' print(ev_survey)
#' @export
evdsi <- function(
site_data, feature_data,
site_detection_columns, site_n_surveys_columns, site_probability_columns,
site_management_cost_column,
site_survey_scheme_column,
site_survey_cost_column,
feature_survey_column,
feature_survey_sensitivity_column,
feature_survey_specificity_column,
feature_model_sensitivity_column,
feature_model_specificity_column,
feature_target_column,
total_budget,
site_management_locked_in_column = NULL,
site_management_locked_out_column = NULL,
prior_matrix = NULL) {
# assert arguments are valid
assertthat::assert_that(
## site_data
inherits(site_data, "sf"), ncol(site_data) > 0,
nrow(site_data) > 0,
## feature_data
inherits(feature_data, "data.frame"), ncol(feature_data) > 0,
nrow(feature_data) > 0,
## site_detection_columns
is.character(site_detection_columns),
length(site_detection_columns) > 0,
assertthat::noNA(site_detection_columns),
all(assertthat::has_name(site_data, site_detection_columns)),
length(site_detection_columns) == nrow(feature_data),
## site_n_surveys_columns
is.character(site_n_surveys_columns),
length(site_n_surveys_columns) > 0,
assertthat::noNA(site_n_surveys_columns),
all(assertthat::has_name(site_data, site_n_surveys_columns)),
length(site_n_surveys_columns) == nrow(feature_data),
## site_probability_columns
is.character(site_probability_columns),
identical(nrow(feature_data), length(site_probability_columns)),
assertthat::noNA(site_probability_columns),
all(assertthat::has_name(site_data, site_probability_columns)),
## site_management_cost_column
assertthat::is.string(site_management_cost_column),
all(assertthat::has_name(site_data, site_management_cost_column)),
is.numeric(site_data[[site_management_cost_column]]),
assertthat::noNA(site_data[[site_management_cost_column]]),
## site_survey_scheme_column
assertthat::is.string(site_survey_scheme_column),
all(assertthat::has_name(site_data, site_survey_scheme_column)),
is.logical(site_data[[site_survey_scheme_column]]),
assertthat::noNA(site_data[[site_survey_scheme_column]]),
sum(site_data[[site_survey_scheme_column]]) >= 1,
## site_survey_cost_column
assertthat::is.string(site_survey_cost_column),
all(assertthat::has_name(site_data, site_survey_cost_column)),
is.numeric(site_data[[site_survey_cost_column]]),
assertthat::noNA(site_data[[site_survey_cost_column]]),
## feature_survey_column
assertthat::is.string(feature_survey_column),
all(assertthat::has_name(feature_data, feature_survey_column)),
is.logical(feature_data[[feature_survey_column]]),
assertthat::noNA(feature_data[[feature_survey_column]]),
sum(feature_data[[feature_survey_column]]) >= 1,
## feature_survey_sensitivity_column
assertthat::is.string(feature_survey_sensitivity_column),
all(assertthat::has_name(feature_data, feature_survey_sensitivity_column)),
is.numeric(feature_data[[feature_survey_sensitivity_column]]),
assertthat::noNA(
feature_data[[feature_survey_sensitivity_column]]),
all(feature_data[[feature_survey_sensitivity_column]] >= 0),
all(feature_data[[feature_survey_sensitivity_column]] <= 1),
## feature_survey_specificity_column
assertthat::is.string(feature_survey_specificity_column),
all(assertthat::has_name(feature_data, feature_survey_specificity_column)),
is.numeric(feature_data[[feature_survey_specificity_column]]),
assertthat::noNA(feature_data[[feature_survey_specificity_column]]),
all(feature_data[[feature_survey_specificity_column]] >= 0),
all(feature_data[[feature_survey_specificity_column]] <= 1),
## feature_model_sensitivity_column
assertthat::is.string(feature_model_sensitivity_column),
all(assertthat::has_name(feature_data, feature_model_sensitivity_column)),
is.numeric(feature_data[[feature_model_sensitivity_column]]),
assertthat::noNA(feature_data[[feature_model_sensitivity_column]]),
all(feature_data[[feature_model_sensitivity_column]] >= 0),
all(feature_data[[feature_model_sensitivity_column]] <= 1),
## feature_model_specificity_column
assertthat::is.string(feature_model_specificity_column),
all(assertthat::has_name(feature_data, feature_model_specificity_column)),
is.numeric(feature_data[[feature_model_specificity_column]]),
assertthat::noNA(feature_data[[feature_model_specificity_column]]),
all(feature_data[[feature_model_specificity_column]] >= 0),
all(feature_data[[feature_model_specificity_column]] <= 1),
## feature_target_column
assertthat::is.string(feature_target_column),
all(assertthat::has_name(feature_data, feature_target_column)),
is.numeric(feature_data[[feature_target_column]]),
assertthat::noNA(feature_data[[feature_target_column]]),
all(feature_data[[feature_target_column]] >= 0),
## total_budget
assertthat::is.number(total_budget), assertthat::noNA(total_budget),
isTRUE(total_budget > 0),
## prior_matrix
inherits(prior_matrix, c("matrix", "NULL")))
## site_management_locked_in_column
if (!is.null(site_management_locked_in_column)) {
assertthat::assert_that(
assertthat::is.string(site_management_locked_in_column),
all(assertthat::has_name(site_data, site_management_locked_in_column)),
is.logical(site_data[[site_management_locked_in_column]]),
assertthat::noNA(site_data[[site_management_locked_in_column]]))
assertthat::assert_that(
sum(site_data[[site_management_locked_in_column]] *
site_data[[site_management_cost_column]]) <=
total_budget,
msg = "cost of managing locked in sites exceeds total budget")
}
## site_management_locked_out_column
if (!is.null(site_management_locked_out_column)) {
assertthat::assert_that(
assertthat::is.string(site_management_locked_out_column),
all(assertthat::has_name(site_data, site_management_locked_out_column)),
is.logical(site_data[[site_management_locked_out_column]]),
assertthat::noNA(site_data[[site_management_locked_out_column]]))
if (all(site_data[[site_management_locked_out_column]]))
warning("all sites locked out")
}
## validate locked arguments if some locked in and some locked out
if (!is.null(site_management_locked_in_column) &&
!is.null(site_management_locked_out_column)) {
assertthat::assert_that(
all(site_data[[site_management_locked_in_column]] +
site_data[[site_management_locked_out_column]] <= 1),
msg = "at least one planning unit is locked in and locked out")
}
## validate targets
validate_target_data(feature_data, feature_target_column)
## validate survey data
validate_site_detection_data(site_data, site_detection_columns)
validate_site_n_surveys_data(site_data, site_n_surveys_columns)
## validate model probability values
validate_site_probability_data(site_data, site_probability_columns)
## verify targets
assertthat::assert_that(
all(feature_data[[feature_target_column]] <= nrow(site_data)))
if (!is.null(site_management_locked_out_column)) {
assertthat::assert_that(
all(feature_data[[feature_target_column]] <=
sum(!site_data[[site_management_locked_out_column]])))
}
# prepare data for analysis
## drop spatial information
if (inherits(site_data, "sf"))
site_data <- sf::st_drop_geometry(site_data)
## calculate prior matrix
if (is.null(prior_matrix)) {
pij <- prior_probability_matrix(
site_data, feature_data, site_detection_columns,
site_n_surveys_columns, site_probability_columns,
feature_survey_sensitivity_column, feature_survey_specificity_column,
feature_model_sensitivity_column, feature_model_specificity_column)
} else {
validate_prior_data(prior_matrix, nrow(site_data), nrow(feature_data))
pij <- prior_matrix
}
## prepare locked in data
if (!is.null(site_management_locked_in_column)) {
site_management_locked_in <- site_data[[site_management_locked_in_column]]
} else {
site_management_locked_in <- rep(FALSE, nrow(site_data))
}
## prepare locked out data
if (!is.null(site_management_locked_out_column)) {
site_management_locked_out <- site_data[[site_management_locked_out_column]]
} else {
site_management_locked_out <- rep(FALSE, nrow(site_data))
}
## validate that targets are feasible given budget and locked out units
sorted_costs <- sort(
site_data[[site_management_cost_column]][!site_management_locked_out])
sorted_costs <- sorted_costs[
seq_len(max(feature_data[[feature_target_column]]))]
assertthat::assert_that(
sum(sorted_costs) <= total_budget,
msg = paste("targets cannot be achieved given budget and locked out",
"planning units"))
# main calculation
out <- rcpp_expected_value_of_decision_given_survey_scheme(
pij = pij,
survey_features = feature_data[[feature_survey_column]],
survey_sensitivity = feature_data[[feature_survey_sensitivity_column]],
survey_specificity = feature_data[[feature_survey_specificity_column]],
pu_survey_solution = site_data[[site_survey_scheme_column]],
pu_survey_costs = site_data[[site_survey_cost_column]],
pu_purchase_costs = site_data[[site_management_cost_column]],
pu_purchase_locked_in = site_management_locked_in,
pu_purchase_locked_out = site_management_locked_out,
obj_fun_target = round(feature_data[[feature_target_column]]),
total_budget = total_budget)
# return result
out
}
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