Function reference • spatialRF

All functions
`auc()`	Area under the ROC curve
`auto_cor()`	Multicollinearity reduction via Pearson correlation
`auto_vif()`	Multicollinearity reduction via Variance Inflation Factor
`beowulf_cluster()`	Defines a beowulf cluster
`case_weights()`	Generates case weights for binary data
`default_distance_thresholds()`	Default distance thresholds to generate spatial predictors
`distance_matrix`	Matrix of distances among ecoregion edges.
`double_center_distance_matrix()`	Double centers a distance matrix
`filter_spatial_predictors()`	Removes redundant spatial predictors
`get_evaluation()`	Gets performance data frame from a cross-validated model
`get_importance()`	Gets the global importance data frame from a model
`get_importance_local()`	Gets the local importance data frame from a model
`get_moran()`	Gets Moran's I test of model residuals
`get_performance()`	Gets out-of-bag performance scores from a model
`get_predictions()`	Gets model predictions
`get_residuals()`	Gets model residuals
`get_response_curves()`	Gets data to allow custom plotting of response curves
`get_spatial_predictors()`	Gets the spatial predictors of a spatial model
`is_binary()`	Checks if dependent variable is binary with values 1 and 0
`make_spatial_fold()`	Makes one training and one testing spatial folds
`make_spatial_folds()`	Makes training and testing spatial folds
`mem()`	Moran's Eigenvector Maps of a distance matrix
`mem_multithreshold()`	Moran's Eigenvector Maps for different distance thresholds
`moran()`	Moran's I test
`moran_multithreshold()`	Moran's I test on a numeric vector for different neighborhoods
`residuals_test()`	Normality test of a numeric vector
`objects_size()`	Shows size of objects in the R environment
`optimization_function()`	Optimization equation to select spatial predictors
`pca()`	Principal Components Analysis
`pca_multithreshold()`	PCA of a distance matrix over distance thresholds
`plant_richness_df`	Plant richness and predictors of American ecoregions
`plot_evaluation()`	Plots the results of a spatial cross-validation
`plot_importance()`	Plots the variable importance of a model
`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
`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
`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
`vif()`	Variance Inflation Factor of a data frame
`weights_from_distance_matrix()`	Transforms a distance matrix into a matrix of weights

Reference

All functions