pROC: Partial ROC calculation for Niche Models

In tenm: Temporal Ecological Niche Models

Partial ROC calculation for Niche Models

Description

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.

Details

Partial ROC is calculated following Peterson et al. (2008; \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ecolmodel.2007.11.008")}). This function is a modification of the PartialROC function, available at https://github.com/narayanibarve/ENMGadgets.

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.

References

Peterson, A.T. et al. (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Modell., 213, 63–72. \Sexpr[results=rd]{tools:::Rd_expr_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)
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)

tenm documentation built on Sept. 11, 2024, 6:34 p.m.