Crossvalidation 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 ModelEvaluationclass
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 ModelEvaluationclass
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 24:3849
threshold
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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