approx_evdsi | R Documentation |
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.
approx_evdsi( 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 )
site_data |
|
feature_data |
|
site_detection_columns |
|
site_n_surveys_columns |
|
site_probability_columns |
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site_management_cost_column |
|
site_survey_scheme_column |
|
site_survey_cost_column |
|
feature_survey_column |
|
feature_survey_sensitivity_column |
|
feature_survey_specificity_column |
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feature_model_sensitivity_column |
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feature_model_specificity_column |
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feature_target_column |
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total_budget |
|
site_management_locked_in_column |
|
site_management_locked_out_column |
|
prior_matrix |
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n_approx_replicates |
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n_approx_outcomes_per_replicate |
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seed |
|
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.
A numeric
vector containing the expected values for each
replicate.
prior_probability_matrix()
.
# 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))
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