plants_rf_spatial: Example fitted spatial random forest model

In spatialRF: Easy Spatial Modeling with Random Forest

Example fitted spatial random forest model

Description

Fitted spatial random forest model using plants_df with spatial predictors from Moran's Eigenvector Maps. Provided for testing and examples without requiring model fitting. Fitted with reduced complexity for faster computation and smaller object size.

Usage

data(plants_rf_spatial)

Format

An object of class rf fitted with the following parameters:

• data: plants_df

• dependent.variable.name: plants_response ("richness_species_vascular")

• predictor.variable.names: plants_predictors (17 variables)

• distance.matrix: plants_distance

• xy: plants_xy

• distance.thresholds: c(100, 1000, 2000, 4000)

• method: "mem.effect.recursive"

• num.trees: 50

• min.node.size: 30

• n.cores: 14

Details

This spatial model includes spatial predictors (Moran's Eigenvector Maps) selected using the recursive method to minimize residual spatial autocorrelation. Uses reduced complexity (50 trees, min.node.size = 30) to keep object size small for package distribution. For actual analyses, use higher values (e.g., num.trees = 500, min.node.size = 5).

See Also

rf_spatial(), rf(), plants_rf, plants_df, plants_response, plants_predictors

Other data: plants_df, plants_distance, plants_predictors, plants_response, plants_rf, plants_xy

spatialRF documentation built on Dec. 20, 2025, 1:07 a.m.