evaluate

0th

Percentile

Model evaluation

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.

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 inheriting from 'DistModel'; not used when x is missing (and both a and p are vectors)

x

Optional. Predictor variables (object of class Raster*). If present, p and a 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 ModelEvaluation-class

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 24:38-49

threshold

• evaluate
Examples
# NOT RUN {
## 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)
# a 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)
# }

Documentation reproduced from package dismo, version 1.3-3, License: GPL (>= 3)

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