This function 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")number of correctly predicted presences
number of absences incorrectly predicted as presences
number of presences incorrectly predicted as absences
number of correctly predicted absences
total number of cases. If NULL (the dafault) it is calculated automatically by adding up a, b, c and d.)
a character vector of length 1 indicating the the evaluation measure to use. Type 'modEvAmethods("threshMeasures")' for available options.
The value of the specified evaluation measure.
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.
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.
# NOT RUN {
evaluate(23, 44, 21, 34)
evaluate(23, 44, 21, 34, measure = "TSS")
# }
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