Cross-validation of models with presence/absence data. Given a vector of presence and a vector of absence values (or a model and presence and absence points and predictors), confusion matrices are computed (for varying thresholds), and model evaluation statistics are computed for each confusion matrix / threshold. See the description of class `ModelEvaluation-class`

for more info.

1 |

`p` |
presence points (x and y coordinates or SpatialPoints* object). Or, if Or, a matrix with values to compute predictions for |

`a` |
absence points (x and y coordinates or SpatialPoints* object). Or, if Or, a matrix with values to compute predictions for |

`model` |
any fitted model, including objects inherting from 'DistModel'; not used when |

`x` |
Optional. Predictor variables (object of class Raster*). If present, |

`tr` |
Optional. a vector of threshold values to use for computing the confusion matrices |

`...` |
Additional arguments for the predict function |

An object of `ModelEvaluation-class`

Robert J. Hijmans

Fielding, A.H. and J.F. Bell, 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24:38-49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
## See ?maxent for an example with real data.
# this is a contrived example:
# p has the predicted values for 50 known cases (locations)
# with presence of the phenomenon (species)
p <- rnorm(50, mean=0.7, sd=0.3)
# b has the predicted values for 50 background locations (or absence)
a <- rnorm(50, mean=0.4, sd=0.4)
e <- evaluate(p=p, a=a)
threshold(e)
plot(e, 'ROC')
plot(e, 'TPR')
boxplot(e)
density(e)
str(e)
``` |

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