Easy Spatial Modeling with Random Forest
Prepares variable importance objects for spatial models
Custom print method for random forest models
Prints cross-validation results
Prints variable importance
Prints results of a Moran's I test
print_performance
Ranks spatial predictors
Rescales a numeric vector into a new range
Normality test of a numeric vector
Random forest models with Moran's I test of the residuals
Compares models via spatial cross-validation
Evaluates random forest models with spatial cross-validation
Contribution of each predictor to model transferability
Fits several random forest models on the same data
Fits spatial random forest models
Tuning of random forest hyperparameters via spatial cross-validation
RMSE and normalized RMSE
Finds optimal combinations of spatial predictors
Sequential introduction of spatial predictors into a model
Standard error of the mean of a numeric vector
Statistical mode of a vector
Suggest variable interactions and composite features for random forest...
Applies thinning to pairs of coordinates
Applies thinning to pairs of coordinates until reaching a given n
Variance Inflation Factor of a data frame
Transforms a distance matrix into a matrix of weights
Area under the ROC curve
Multicollinearity reduction via Pearson correlation
Multicollinearity reduction via Variance Inflation Factor
Defines a beowulf cluster
Generates case weights for binary data
Default distance thresholds to generate spatial predictors
Double centers a distance matrix
Removes redundant spatial predictors
Gets performance data frame from a cross-validated model
Gets the global importance data frame from a model
Gets the local importance data frame from a model
Gets Moran's I test of model residuals
Gets out-of-bag performance scores from a model
Gets model predictions
Gets model residuals
Gets data to allow custom plotting of response curves
Gets the spatial predictors of a spatial model
Checks if dependent variable is binary with values 1 and 0
Makes one training and one testing spatial folds
Makes training and testing spatial folds
Moran's Eigenvector Maps of a distance matrix
Moran's Eigenvector Maps for different distance thresholds
Moran's I test
Moran's I test on a numeric vector for different neighborhoods
Normality test of a numeric vector
Shows size of objects in the R environment
Optimization equation to select spatial predictors
Principal Components Analysis
PCA of a distance matrix over distance thresholds
Plots the results of a spatial cross-validation
Plots the variable importance of a model
Plots a Moran's I test of model residuals
Optimization plot of a selection of spatial predictors
Plot residuals diagnostics
Plots the response curves of a model.
Plots the response surfaces of a random forest model
Scatterplots of a training data frame
Moran's I plots of a training data frame
Plots a tuning object produced by rf_tuning()
Automatic generation and selection of spatial predictors for spatial regression with Random Forest. Spatial predictors are surrogates of variables driving the spatial structure of a response variable. The package offers two methods to generate spatial predictors from a distance matrix among training cases: 1) Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>): computed as the eigenvectors of a weighted matrix of distances; 2) RFsp (Hengl et al. <DOI:10.7717/peerj.5518>): columns of the distance matrix used as spatial predictors. Spatial predictors help minimize the spatial autocorrelation of the model residuals and facilitate an honest assessment of the importance scores of the non-spatial predictors. Additionally, functions to reduce multicollinearity, identify relevant variable interactions, tune random forest hyperparameters, assess model transferability via spatial cross-validation, and explore model results via partial dependence curves and interaction surfaces are included in the package. The modelling functions are built around the highly efficient 'ranger' package (Wright and Ziegler 2017 <DOI:10.18637/jss.v077.i01>).