Package index • spatialRF

Main modeling functions

Primary entry points for fitting random forest and spatial random forest models.

Functions for model comparison, evaluation, tuning, and advanced modeling operations.

rf_compare(): Compares models via spatial cross-validation
rf_evaluate(): Evaluates random forest models with spatial cross-validation
rf_importance(): Contribution of each predictor to model transferability
rf_repeat(): Fits several random forest models on the same data
rf_tuning(): Tuning of random forest hyperparameters via spatial cross-validation

Functions for variable selection, multicollinearity reduction, distance matrix manipulation, and spatial fold creation.

auto_cor(): Multicollinearity reduction via Pearson correlation
auto_vif(): Multicollinearity reduction via Variance Inflation Factor
case_weights(): Generate case weights for imbalanced binary data
default_distance_thresholds(): Default distance thresholds for spatial predictors
double_center_distance_matrix(): Double-center a distance matrix
is_binary(): Check if variable is binary with values 0 and 1
make_spatial_fold(): Create spatially independent training and testing folds
make_spatial_folds(): Create multiple spatially independent training and testing folds
the_feature_engineer(): Suggest variable interactions and composite features for random forest models
weights_from_distance_matrix(): Transforms a distance matrix into a matrix of weights

Functions for generating spatial predictors (MEMs, PCA), testing spatial autocorrelation (Moran’s I), and selecting/filtering spatial predictors.

filter_spatial_predictors(): Remove redundant spatial predictors
mem(): Compute Moran's Eigenvector Maps from distance matrix
mem_multithreshold(): Compute Moran's Eigenvector Maps across multiple distance thresholds
moran(): Moran's I test for spatial autocorrelation
moran_multithreshold(): Moran's I test across multiple distance thresholds
residuals_test(): Normality test of a numeric vector
pca(): Compute Principal Component Analysis
pca_multithreshold(): Compute Principal Component Analysis at multiple distance thresholds
rank_spatial_predictors(): Ranks spatial predictors
residuals_diagnostics(): Normality test of a numeric vector
select_spatial_predictors_recursive(): Finds optimal combinations of spatial predictors
select_spatial_predictors_sequential(): Sequential introduction of spatial predictors into a model

Functions to extract model components and print results.

Functions for creating diagnostic, exploratory, and results plots.

plot_evaluation(): Visualize spatial cross-validation results
plot_importance(): Visualize variable importance scores
plot_moran(): Plots a Moran's I test of model residuals
plot_optimization(): Optimization plot of a selection of spatial predictors
plot_residuals_diagnostics(): Plot residuals diagnostics
plot_response_curves(): Plots the response curves of a model.
plot_response_surface(): Plots the response surfaces of a random forest model
plot_training_df(): Scatterplots of a training data frame
plot_training_df_moran(): Moran's I plots of a training data frame
plot_tuning(): Plots a tuning object produced by rf_tuning()

Low-level helper functions for statistical computations and parallel execution.

auc(): Area under the ROC curve
beowulf_cluster(): Create a Beowulf cluster for parallel computing
.vif_to_df(): Convert VIF values to data frame
objects_size(): Display sizes of objects in current R environment
optimization_function(): Compute optimization scores for spatial predictor selection
prepare_importance_spatial(): Prepares variable importance objects for spatial models
rescale_vector(): Rescales a numeric vector into a new range
root_mean_squared_error(): RMSE and normalized RMSE
setup_parallel_execution(): Setup parallel execution with automatic backend detection
standard_error(): Standard error of the mean of a numeric vector
statistical_mode(): Statistical mode of a vector
thinning(): Applies thinning to pairs of coordinates
thinning_til_n(): Applies thinning to pairs of coordinates until reaching a given n

Datasets for testing and learning spatialRF functionality.