Skip to contents

Partial ROC calculation for Niche Models

Source: R/PartialROC.R
pROC.Rd

Apply partial ROC tests to continuous niche models.

Usage

pROC(
  continuous_mod,
  test_data,
  n_iter = 1000,
  E_percent = 5,
  boost_percent = 50,
  rseed = FALSE,
  sub_sample = TRUE,
  sub_sample_size = 1000
)

Arguments

continuous_mod

A SpatRaster or numeric vector of the ecological niche model to be evaluated. If a numeric vector is provided, it should contain the values of the predicted suitability.

test_data

A numerical matrix, data.frame, or numeric vector:

  • If data.frame or matrix, it should contain coordinates of the occurrences used to test the ecological niche model. Columns must be: longitude and latitude.

  • If numeric vector, it should contain the values of the predicted suitability.

n_iter

Number of bootstrap iterations to perform for partial ROC calculations. Default is 1000.

E_percent

Numeric value from 0 to 100 used as the threshold (E) for partial ROC calculations. Default is 5.

boost_percent

Numeric value from 0 to 100 representing the percentage of testing data to use for bootstrap iterations in partial ROC. Default is 50.

rseed

Logical. Whether or not to set a random seed for reproducibility. Default is FALSE.

sub_sample

Logical. Indicates whether to use a subsample of the test data. Recommended for large datasets.

sub_sample_size

Size of the subsample to use for computing pROC values when sub_sample is TRUE.

Value

A list of two elements:

  • "pROC_summary": a data.frame containing the mean AUC value, AUC ratio calculated for each iteration and the p-value of the test.

  • "pROC_results": a data.frame with four columns containing the AUC (auc_model), partial AUC (auc_pmodel), partial AUC of the random model (auc_prand) and the AUC ratio (auc_ratio) for each iteration.

Details

Partial ROC is calculated following Peterson et al. (2008; doi:10.1016/j.ecolmodel.2007.11.008 ). This function is a modification of the PartialROC function, available at https://github.com/narayanibarve/ENMGadgets.

References

Peterson, A.T. et al. (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Modell., 213, 63–72. doi:10.1016/j.ecolmodel.2007.11.008

Examples

data(abronia)
suit_1970_2000 <- terra::rast(system.file("extdata/suit_1970_2000.tif",
                                          package = "tenm"))
print(suit_1970_2000)
#> class       : SpatRaster 
#> dimensions  : 23, 20, 1  (nrow, ncol, nlyr)
#> resolution  : 0.1666667, 0.1666667  (x, y)
#> extent      : -99.16667, -95.83333, 17.16667, 21  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326) 
#> source      : suit_1970_2000.tif 
#> name        : suit_1970_2000 
#> min value   :   4.614139e-37 
#> max value   :   8.332142e-01 
proc_test <- tenm::pROC(continuous_mod = suit_1970_2000,
                        test_data = abronia[,c("decimalLongitude",
                                               "decimalLatitude")],
                        n_iter = 500, E_percent=5,
                        boost_percent=50)
print(proc_test$pROC_summary)
#>              Mean_AUC Mean_pAUC_ratio_at_5%               P_value 
#>             0.9024062             1.5066030             0.0000000