Create prior probability matrix for the value of information analysis.
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
)sf::sf() object with site data.
base::data.frame() object with feature data.
character names of numeric
columns in the argument to site_data that contain the proportion of
surveys conducted within each site that detected each feature.
Each column should correspond to a different feature, and contain
a proportion value (between zero and one). If a site has
not previously been surveyed, a value of zero should be used.
character names of numeric
columns in the argument to site_data that contain the total
number of surveys conducted for each each feature within each site.
Each column should correspond to a different feature, and contain
a non-negative integer number (e.g. 0, 1, 2, 3). If a site has
not previously been surveyed, a value of zero should be used.
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.
character name of the
column in the argument to feature_data that contains
probability of future surveys correctly detecting a presence of each
feature in a given site (i.e. the sensitivity of the survey methodology).
This column should have numeric values that are between zero and
one. No missing (NA) values are permitted in this column.
character name of the
column in the argument to feature_data that contains
probability of future surveys correctly detecting an absence of each
feature in a given site (i.e. the specificity of the survey methodology).
This column should have numeric values that are between zero and
one. No missing (NA) values are permitted in this 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().
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().
A matrix object containing the prior probabilities of each
feature occupying each site. Each row corresponds to a different
feature and each column corresponds to a different site.
# set seeds for reproducibility
set.seed(123)
# load example site data
data(sim_sites)
print(sim_sites)
#> Simple feature collection with 6 features and 13 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 0.10513 ymin: 0.04556193 xmax: 0.9764926 ymax: 0.8637977
#> CRS: NA
#> # A tibble: 6 × 14
#> survey_cost management_cost f1 f2 f3 n1 n2 n3 e1 e2
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 102 1 1 1 3 3 3 1.00 -0.848
#> 2 25 90 0 0 0 0 0 0 -1.44 1.27
#> 3 25 165 1 0.6 0 5 5 5 1.25 0.817
#> 4 17 104 0 0 0 0 0 0 -0.484 -0.292
#> 5 18 100 0 0 0 0 0 0 0.0135 0.380
#> 6 15 94 0 0 0 0 0 0 -0.347 -1.33
#> # ℹ 4 more variables: p1 <dbl>, p2 <dbl>, p3 <dbl>, geometry <POINT>
# load example feature data
data(sim_features)
print(sim_features)
#> # A tibble: 3 × 7
#> name survey survey_sensitivity survey_specificity model_sensitivity
#> <chr> <lgl> <dbl> <dbl> <dbl>
#> 1 f1 TRUE 0.954 0.886 0.718
#> 2 f2 TRUE 0.974 0.875 0.705
#> 3 f3 TRUE 0.956 0.823 0.768
#> # ℹ 2 more variables: model_specificity <dbl>, target <dbl>
# calculate prior probability matrix
prior_matrix <- prior_probability_matrix(
sim_sites, sim_features,
c("f1", "f2", "f3"), c("n1", "n2", "n3"), c("p1", "p2", "p3"),
"survey_sensitivity", "survey_specificity",
"model_sensitivity", "model_specificity")
# preview prior probability matrix
print(prior_matrix)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> f1 0.9983146 0.2580030 9.999761e-01 0.2580030 0.2580030 0.2580030
#> f2 0.9978829 0.2556825 2.948501e-01 0.8342984 0.2556825 0.8342984
#> f3 0.9936754 0.2072052 4.363968e-07 0.8716228 0.2072052 0.8716228