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Creates a 'recipe' object for Generalized Additive Models (GAM) in SDM.

Source: R/h3sdm_recipe_gam.R
h3sdm_recipe_gam.Rd

This function prepares an sf (Simple Features) object for use in a Species Distribution Model (SDM) workflow with the 'mgcv' GAM engine within the 'tidymodels' ecosystem.

The crucial step is extracting the coordinates (x, y) from the geometry and assigning them the predictor role so they can be used in the GAM's spatial smooth term (s(x, y, bs = "tp")). It also assigns special roles to the 'presence' and 'h3_address' variables.

Usage

h3sdm_recipe_gam(data)

Arguments

data

An sf (Simple Features) object containing the species presence/absence/abundance data, environmental variables (e.g., bioclimatic), and the geometry (e.g., H3 centroids or points).

Value

A recipe object of class h3sdm_recipe_gam, ready to be chained with additional preprocessing steps (e.g., normalization).

Details

Assigned Roles:

  • outcome: "presence" (or the column containing the response variable).

  • id: "h3_address" (cell identifier, not used for modeling).

  • predictor: All other variables, including x and y for the GAM's smoothing function.

Note on x and y: The x and y coordinates are added to the recipe's internal data frame and are defined as predictor to meet the requirements of the mgcv engine.

See also

Other h3sdm_tools: h3sdm_stack_fit(), h3sdm_workflow_gam()

Examples

if (FALSE) { # \dontrun{
# Assuming 'data_sf' is your sf object with 'presence' and 'h3_address'
# gam_rec <- h3sdm_recipe_gam(data_sf)
# Add normalization only to bio variables, excluding x and y
# final_rec <- gam_rec %>% step_normalize(starts_with("bio"))
} # }