Description Usage Arguments Details Value Author(s) References See Also Examples

This functions evaluates the discrimination performance of a model based on the values of a confusion matrix obtained at a particular threshold.

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`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 discrimination 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.

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