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.
evaluate(p, a, model, x, tr)
- presence points (x and y coordinates or SpatialPoints* object).
xis missing, values at presence points Or, a matrix with values to compute predictions for
- absence points (x and y coordinates or SpatialPoints* object).
xis missing, values at presence points. Or, a matrix with values to compute predictions for
- any fitted model, including objects inherting from 'DistModel'; not used when
- Optional. Predictor variables (object of class Raster*). If present,
aare interpreted as (spatial) points
- Optional. a vector of threshold values to use for computing the confusion matrices
- An object of class ModelEvaluation
# 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)