Easy Spatial Modeling with Random Forest

Documentation for package ‘spatialRF’ version 1.1.5

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.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