evaluate
From dismo v1.14
by Robert Hijmans
Model evaluation
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.
 Keywords
 spatial
Usage
evaluate(p, a, model, x, tr, ...)
Arguments
 p
 presence points (x and y coordinates or SpatialPoints* object).
Or, if
x
is missing, values at presence points Or, a matrix with values to compute predictions for  a
 absence points (x and y coordinates or SpatialPoints* object).
Or, if
x
is missing, values at presence points. Or, a matrix with values to compute predictions for  model
 any fitted model, including objects inherting from 'DistModel'; not used when
x
is missing  x
 Optional. Predictor variables (object of class Raster*). If present,
p
anda
are interpreted as (spatial) points  tr
 Optional. a vector of threshold values to use for computing the confusion matrices
 ...
 Additional arguments for the predict function
Value

An object of
ModelEvaluationclass
References
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
See Also
Examples
## 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)
Community examples
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