Easy Spatial Modeling with Random Forest

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Documentation for package ‘spatialRF’ version 1.1.4

• DESCRIPTION file.
• Package NEWS.

Help Pages

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
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
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
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