evaluate {modEvA} | R Documentation |
This functions evaluates the classification performance of a model based on the values of a confusion matrix obtained at a particular threshold.
evaluate(a, b, c, d, N = NULL, measure = "CCR")
a |
number of correctly predicted presences |
b |
number of absences incorrectly predicted as presences |
c |
number of presences incorrectly predicted as absences |
d |
number of correctly predicted absences |
N |
total number of cases. If NULL (the dafault) it is calculated automatically by adding up a, b, c and d.) |
measure |
a character vector of length 1 indicating the the evaluation measure to use. Type |
A number of measures can be used to evaluate continuous model predictions against observed binary occurrence data (Fielding & Bell 1997; Liu et al. 2011; Barbosa et al. 2013). The evaluate
function can calculate a few threshold-based classification measures from the values of a confusion matrix obtained at a particular threshold. The evaluate
function is used internally by threshMeasures
. It can also be accessed directly by the user, but it is usually more practical to use threshMeasures
, which calculates the confusion matrix automatically.
The value of the specified evaluation measure.
A. Marcia Barbosa
Barbosa A.M., Real R., Munoz A.R. & Brown J.A. (2013) New measures for assessing model equilibrium and prediction mismatch in species distribution models. Diversity and Distributions, 19: 1333-1338
Fielding A.H. & Bell J.F. (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24: 38-49
Liu C., White M., & Newell G. (2011) Measuring and comparing the accuracy of species distribution models with presence-absence data. Ecography, 34, 232-243.
evaluate(23, 44, 21, 34) evaluate(23, 44, 21, 34, measure = "TSS")