Classify predictions based on an optimal threshold

Converts continuous probability predictions into binary presence/absence based on a specified threshold.

Usage

h3sdm_classify(predictions_sf, threshold)

Arguments

predictions_sf: An sf object containing a numeric column named prediction, typically produced by h3sdm_predict().
threshold: A numeric value representing the probability threshold (e.g., 0.45) above which predictions are classified as presence (1).

Value

An sf object with the same geometry and all original columns, plus a new integer column predicted_presence with values 0 (absence) or 1 (presence).

Details

This function is useful for converting continuous probability outputs into binary presence/absence data for mapping or model evaluation purposes.

Examples

if (FALSE) { # \dontrun{
library(sf)
library(dplyr)

# Crear un sf de ejemplo
df <- data.frame(
  id = 1:5,
  prediction = c(0.2, 0.6, 0.45, 0.8, 0.3),
  lon = c(-75, -74, -73, -72, -71),
  lat = c(10, 11, 12, 13, 14)
)

df_sf <- st_as_sf(df, coords = c("lon", "lat"), crs = 4326)

# Clasificar usando un umbral
classified_sf <- h3sdm_classify(df_sf, threshold = 0.5)

# Revisar resultados
print(classified_sf)
} # }