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.
evaluate(p, a, model, x, tr, ...)
- presence points (x and y coordinates or SpatialPoints* object).
xis missing, values at presence points Or, a matrix with values to compute predictions for
- absence points (x and y coordinates or SpatialPoints* object).
xis missing, values at presence points. Or, a matrix with values to compute predictions for
- any fitted model, including objects inherting from 'DistModel'; not used when
- Optional. Predictor variables (object of class Raster*). If present,
aare interpreted as (spatial) points
- Optional. a vector of threshold values to use for computing the confusion matrices
- Additional arguments for the predict function
An object of
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
## 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)