Prior probability matrix — prior_probability

Create prior probability matrix for the value of information analysis.

Usage

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
)

Arguments

site_data: sf::sf() object with site data.
feature_data: base::data.frame() object with feature data.
site_detection_columns: 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.
site_n_surveys_columns: 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.
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.
feature_survey_sensitivity_column: 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.
feature_survey_specificity_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.
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().
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().

Value

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.

Examples

# 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: 0.02541313 ymin: 0.07851093 xmax: 0.9888107 ymax: 0.717068
#> 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          19              99     0     0  0        0     0     0  1.13   0.535
#> 2          22              87     0     1  0.25     4     4     4 -1.37  -1.45 
#> 3          13              94     1     1  0        1     1     1  0.155 -0.867
#> 4          19              61     0     0  0        0     0     0 -0.792  1.32 
#> 5           9             105     0     0  0        0     0     0 -0.194  0.238
#> 6          12             136     0     0  0        0     0     0  1.07   0.220
#> # 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                0.951              0.854             0.711
#> 2 f2    TRUE                0.990              0.832             0.722
#> 3 f3    TRUE                0.986              0.808             0.772
#> # 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 0.8173204 1.079836e-05 0.8673022 0.2554465 0.2554465 0.8173204
#> f2 0.7883133 9.991649e-01 0.8546773 0.2567227 0.2567227 0.7883133
#> f3 0.2077566 2.583338e-05 0.0168493 0.8563573 0.8563573 0.2077566