AUCuniform_trap {vandalico} | R Documentation |
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
)
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,
|
deleteBins |
A vector (e.g., from 1 to 10 if |
w |
A vector with the weights associated to each case. If |
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
.
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
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).