R/approx_evdsi.R

In surveyvoi: Survey Value of Information

#' Approximate 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 conducts a surveys a
#' set of sites to inform the decision. To speed up the calculations,
#' an approximation method is used.
#'
#' @inheritParams evdsi
#'
#' @param n_approx_replicates `integer` number of replicates to use for
#'   approximating the expected value calculations. Defaults to 100.
#'
#' @param n_approx_outcomes_per_replicate `integer` number of outcomes to
#'   use per replicate for approximation calculations. Defaults to 10000.
#'
#' @param seed `integer` state of the random number generator for
#'  simulating outcomes when conducting the value of information analyses.
#'  Defaults to 500.
#'
#' @details This function uses approximation methods to estimate the
#'   expected value calculations. The accuracy of these
#'   calculations depend on the arguments to
#'   `n_approx_replicates` and `n_approx_outcomes_per_replicate`, and
#'   so you may need to increase these parameters for large problems.
#'
#' @return A `numeric` vector containing the expected values for each
#' replicate.
#'
#' @inherit evdci seealso
#'
#' @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 approximation method
#' approx_ev_survey <- approx_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 mean value
#' print(mean(approx_ev_survey))
#' @export
approx_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,
  n_approx_replicates = 100,
  n_approx_outcomes_per_replicate = 10000,
  seed = 500) {
  # 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")),
    ## n_approx_replicates
    assertthat::is.count(n_approx_replicates),
    assertthat::noNA(n_approx_replicates),
    ## n_approx_outcomes_per_replicate
    assertthat::is.count(n_approx_outcomes_per_replicate),
    assertthat::noNA(n_approx_outcomes_per_replicate),
    ## seed
    assertthat::is.number(seed))
  ## 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
  withr::with_seed(seed, {
    out <- rcpp_approx_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,
      n_approx_replicates = n_approx_replicates,
      n_approx_outcomes_per_replicate = n_approx_outcomes_per_replicate,
      seed = seed)
  })
  # return result
  out
}