modEvA (version 3.0)

confusionLabel: Label predictions according to their confusion matrix category

Description

This function labels the predictions of a binary-response model according to their confusion matrix categories, i.e., it classifies each prediction into a false positive, false negative, true positive or true negative, given a user-defined threshold value.

Usage

confusionLabel(model = NULL, obs = NULL, pred = NULL, thresh, verbosity = 2)

Arguments

model

a binary-response model object of class "glm", "gam", "gbm", "randomForest" or "bart". If provided, 'obs' and 'pred' will be extracted with mod2obspred. Alternatively, you can input the 'obs' and 'pred' arguments instead of 'model'.

obs

alternatively to 'model' and together with 'pred', a vector of observed presences (1) and absences (0) of a binary response variable. This argument is ignored if 'model' is provided.

pred

alternatively to 'model' and together with 'obs', a vector with the corresponding predicted values of presence probability, habitat suitability, environmental favourability or alike. Must be of the same length and in the same order as 'obs'. This argument is ignored if 'model' is provided.

thresh

numeric value of the threshold to separate predicted presences from predicted absences; can be "preval", to use the prevalence of 'obs' (or of the response variable in 'model') as the threshold, or any real number between 0 and 1. See Details in threshMeasures for an informed choice.

verbosity

integer specifying the amount of messages to display. Defaults to the maximum implemented; lower numbers (down to 0) decrease the number of messages.

Value

This function returns a character vector of the same length as 'obs' and 'pred', or of the same number of rows as the data in 'model', containing the confusion matrix label for each value.

See Also

threshMeasures

Examples

# NOT RUN {
# load sample models:
data(rotif.mods)

# choose a particular model to play with:
mod <- rotif.mods$models[[1]]

confusionLabel(model = mod, thresh = 0.5)


# you can also use confusionLabel with vectors of observed and predicted values
# instead of with a model object:

presabs <- mod$y
prediction <- mod$fitted.values

confusionLabel(obs = presabs, pred = prediction, thresh = 0.5)
# }