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spatialRF
View on CRAN: Click
here
Download and install spatialRF package within the R console
Install from CRAN:
install.packages("spatialRF")
Install from Github:
library("remotes")
install_github("cran/spatialRF")
Install by package version:
library("remotes")
install_version("spatialRF", "1.1.4")
Attach the package and use:
library("spatialRF")
Maintained by
Blas M. Benito
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.spatialRF . https://github.com/BlasBenito/spatialRF/issues/ . https://blasbenito.github.io/spatialRF/ . spatialRF citation info . spatialRF results . spatialRF.pdf . spatialRF_1.1.4.tar.gz . spatialRF_1.1.4.zip . spatialRF_1.1.4.zip . spatialRF_1.1.4.zip . spatialRF_1.1.4.tgz . spatialRF_1.1.4.tgz . spatialRF_1.1.4.tgz . spatialRF_1.1.4.tgz . spatialRF_1.1.4.tgz . spatialRF_1.1.4.tgz . spatialRF archive . https://CRAN.R-project.org/package=spatialRF .
First Published: 2021-09-23
Latest Update: 2022-08-19
Description:
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>).
How to cite:
Blas M. Benito (2021). spatialRF: Easy Spatial Modeling with Random Forest. R package version 1.1.4, https://cran.r-project.org/web/packages/spatialRF. Accessed 12 Apr. 2025.
Previous versions and publish date:
1.1.3 (2021-09-23 10:30)
Other packages that cited spatialRF R package
View spatialRF citation profile
Other R packages that spatialRF depends,
imports, suggests or enhances
Complete documentation for spatialRF
Functions, R codes and Examples using
the spatialRF R package
Some associated functions: auc . auto_cor . auto_vif . beowulf_cluster . case_weights . default_distance_thresholds . distance_matrix . double_center_distance_matrix . filter_spatial_predictors . get_evaluation . get_importance . get_importance_local . get_moran . get_performance . get_predictions . get_residuals . get_response_curves . get_spatial_predictors . is_binary . make_spatial_fold . make_spatial_folds . mem . mem_multithreshold . moran . moran_multithreshold . normality . objects_size . optimization_function . pca . pca_multithreshold . plant_richness_df . plot_evaluation . plot_importance . plot_moran . plot_optimization . plot_residuals_diagnostics . plot_response_curves . plot_response_surface . plot_training_df . plot_training_df_moran . plot_tuning . prepare_importance_spatial . print . print_evaluation . print_importance . print_moran . print_performance . rank_spatial_predictors . rescale_vector . residuals_diagnostics . rf . rf_compare . rf_evaluate . rf_importance . rf_repeat . rf_spatial . rf_tuning . root_mean_squared_error . select_spatial_predictors_recursive . select_spatial_predictors_sequential . standard_error . statistical_mode . the_feature_engineer . thinning . thinning_til_n . vif . weights_from_distance_matrix .
Some associated R codes: auc.R . auto_cor.R . auto_vif.R . beowulf_cluster.R . case_weights.R . default_distance_thresholds.R . distance_matrix.R . double_center_distance_matrix.R . filter_spatial_predictors.R . get_evaluation.R . get_importance.R . get_importance_local.R . get_moran.R . get_performance.R . get_predictions.R . get_residuals.R . get_response_curves.R . get_spatial_predictors.R . is_binary.R . make_spatial_fold.R . make_spatial_folds.R . mem.R . mem_multithreshold.R . moran.R . moran_multithreshold.R . objects_size.R . optimization_function.R . pca.R . pca_multithreshold.R . plant_richness_df.R . plot_evaluation.R . plot_importance.R . plot_moran.R . plot_optimization.R . plot_residuals_diagnostics.R . plot_response_curves.R . plot_response_surface.R . plot_training_df.R . plot_training_df_moran.R . plot_tuning.R . prepare_importance_spatial.R . print.R . print_evaluation.R . print_importance.R . print_moran.R . print_performance.R . rank_spatial_predictors.R . rescale_vector.R . residuals_diagnostics.R . residuals_test.R . rf.R . rf_compare.R . rf_evaluate.R . rf_importance.R . rf_repeat.R . rf_spatial.R . rf_tuning.R . root_mean_squared_error.R . select_spatial_predictors_recursive.R . select_spatial_predictors_sequential.R . standard_error.R . statistical_mode.R . the_feature_engineer.R . thinning.R . thinning_til_n.R . vif.R . weights_from_distance_matrix.R . Full spatialRF package functions and examples
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