vandalico (version 0.2.0)

AUCuniform_trap: Calculates the uniform AUC and uniform Se* by the trapezoidal method.

Description

This function computes the uniform AUC (uAUC) and uniform Se* (uSe*) using the weighted trapezoidal method instead of the weighted bootstrapping method used in AUCuniform and originally proposed in Jiménez-Valverde (2022). This procedure reduces bias and improves the coverage of confidence intervals (Jiménez-Valverde 2024). Additionally, the weights vector associated to each case can be customized. See Jiménez-Valverde (2024) for details.

Usage

AUCuniform_trap(
  mat,
  by = 0.1,
  deleteBins = NULL,
  w = NULL,
  plot = FALSE,
  plot.compare = FALSE,
  plot.adds = FALSE
)

Value

A list with the following elements:

AUC: the AUC value (non-uniform, unweighted), a numeric value between 0 and 1.

Se: the Se* value (non-uniform, unweighted), a numeric value between 0 and 1.

bins: a table with the sample size of each bin (only if w = NULL).

uAUC: the uniform AUC value (only if w = NULL).

uSe: the uniform Se* value (only if w = NULL).

wAUC: the weighted AUC estimated with the vector w

wSe: the weighted Se* estimated with the vector w

Arguments

mat

A matrix with two columns. The first column must contain the suitability values (i.e., the classification rule); the second column must contain the presences and absences.

by

Size of the suitability intervals (i.e., bins). By default, by = 0.1.

deleteBins

A vector (e.g., from 1 to 10 if by = 0.1) with the bins that have to be excluded (1 for [0,0.1), 10 for [0.9,1]) from the calculation of the uniform statistics; NULL by default.

w

A vector with the weights associated to each case. If NULL (default), then the uniform AUC (uAUC) and uniform Se* (uSe*) are calculated.

plot

Logical. Indicates whether or not the observed ROC curve is plotted (gray dots).

plot.compare

Logical. Indicates whether or not the weighed ROC curve is plotted (black line).

plot.adds

Logical. Indicates whether or not the negative diagonal and the points of equivalence (weighted and unweighted) are added to the ROC plot.

Details

This function calculates the uniform AUC (uAUC) and uniform Se* (uSe*) using the weighted trapezoidal method as suggested in Jiménez-Valverde (2024). A warning message will be shown if the sample size of any bin is zero. Another warning message will be shown if the sample size of any bin is lower than 15. In such case, trimming should be considered using deleteBins (Jiménez-Valverde 2022). Alternatively, the weights associated to each case can be fully customized with the w parameter (Jiménez-Valverde 2024). In this case, no warnings regarding sample size issues are raised, and deleteBins is not used. The AUC (non-uniform, unweighted) is estimated non-parametrically by the trapezoidal rule, which is equivalent to the Wilcoxon-based estimation (Hanley & McNeil 1982) used in AUCuniform. Se* is calculated as in AUCuniform.

References

Hanley, J. A. & McNeil, B. J. (1982). The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve. Radiology., 143, 29-36.

Jiménez-Valverde, A. (2022). The uniform AUC: dealing with the representativeness effect in presence-absence models. Methods Ecol. Evol., 13, 1224-1236.

Jiménez-Valverde, A. (2024). Improving the uniform AUC (uAUC): towards a case-by-case weighting evaluation in species distribution models. In preparation.

Examples

suit<-rbeta(100, 2, 2) #Generate suitability values
random<-runif(100)
sp<-ifelse(random < suit, 1, 0) #Generate presence-absence data
result<-AUCuniform_trap(cbind(suit, sp), plot = TRUE, plot.compare = TRUE)
result$AUC #Get the AUC
result$uAUC #Get the uAUC. Note how it is closer to the reference value of
            #0.83 since the suitability values are simulated to be
            #well-calibrated (see Jimenez-Valverde 2022).