R: Easy Spatial Modeling with Random Forest

# Easy Spatial Modeling with Random Forest

## Documentation for package ‘spatialRF’ version 1.1.5

- DESCRIPTION file
- Package NEWS

## Help Pages

.vif_to_df | Convert VIF values to data frame
auc | Area under the ROC curve
auto_cor | Multicollinearity reduction via Pearson correlation
auto_vif | Multicollinearity reduction via Variance Inflation Factor
beowulf_cluster | Create a Beowulf cluster for parallel computing
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
filter_spatial_predictors | Remove redundant spatial predictors
get_evaluation | Extract evaluation metrics from cross-validated model
get_importance | Extract variable importance from model
get_importance_local | Extract local variable importance from model
get_moran | Extract Moran's I test results for model residuals
get_performance | Extract out-of-bag performance metrics from model
get_predictions | Extract fitted predictions from model
get_residuals | Extract model residuals
get_response_curves | Extract response curve data for plotting
get_spatial_predictors | Extract spatial predictors from spatial model
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
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
objects_size | Display sizes of objects in current R environment
optimization_function | Compute optimization scores for spatial predictor selection
pca | Compute Principal Component Analysis
pca_multithreshold | Compute Principal Component Analysis at multiple distance thresholds
plants_df | Plant richness and predictors for American ecoregions
plants_distance | Distance matrix between ecoregion edges
plants_predictors | Predictor variable names for plant richness examples
plants_response | Response variable name for plant richness examples
plants_rf | Example fitted random forest model
plants_rf_spatial | Example fitted spatial random forest model
plants_xy | Coordinates for plant richness data
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()'
prepare_importance_spatial | Prepares variable importance objects for spatial models
print.rf | Custom print method for random forest models
print_evaluation | Prints cross-validation results
print_importance | Prints variable importance
print_moran | Prints results of a Moran's I test
print_performance | print_performance
rank_spatial_predictors | Ranks spatial predictors
rescale_vector | Rescales a numeric vector into a new range
residuals_diagnostics | Normality test of a numeric vector
residuals_test | Normality test of a numeric vector
rf | Random forest models with Moran's I test of the residuals
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_spatial | Fits spatial random forest models
rf_tuning | Tuning of random forest hyperparameters via spatial cross-validation
root_mean_squared_error | RMSE and normalized RMSE
select_spatial_predictors_recursive | Finds optimal combinations of spatial predictors
select_spatial_predictors_sequential | Sequential introduction of spatial predictors into a model
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
the_feature_engineer | Suggest variable interactions and composite features for random forest models
thinning | Applies thinning to pairs of coordinates
thinning_til_n | Applies thinning to pairs of coordinates until reaching a given n
weights_from_distance_matrix | Transforms a distance matrix into a matrix of weights