# evaluate

##### 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 pointsOr, 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

##### See Also

##### 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)*