# evaluate

From dismo v0.5-1

##### Model testing

Model testing for models predicting presence/absence. Given a vector of presence and a vector of absence values (or a model and presence and absence points and predictors), a number of confusion matrices is computed (for varying thresholds), and model evaluation statistics are computed for each confusion matrix / threshold.

- 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`

and`a`

are interpreted as (spatial) points - tr
- Optional. a vector of threshold values to use for computing the confusion matrices

##### Value

- An object of class ModelEvaluation

##### Examples

```
# p = the predicted value for 50 known cases (locations) with presence of the phenomenon (species)
p = rnorm(50, mean=0.7, sd=0.3)
# b = the predicted value for 50 known cases (locations) with absence of the phenomenon (species)
a = rnorm(50, mean=0.4, sd=0.4)
e = evaluate(p=p, a=a)
# threshold at maximum kappa
e@t[which.max(e@kappa)]
# threshold at maximum of the sum of the sensitivity (true positive rate) and specificity (true negative rate)
e@t[which.max(e@TPR + e@TNR)]
plot(e, 'ROC')
plot(e, 'TPR')
boxplot(e)
density(e)
```

*Documentation reproduced from package dismo, version 0.5-1, License: GPL (>= 3)*

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