Other packages > Find by keyword >

spatialRF  

Easy Spatial Modeling with Random Forest
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]
All associated links for this package
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
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
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
Downloads during the last 30 days
Get rewarded with contribution points by helping add
Reviews / comments / questions /suggestions ↴↴↴

Today's Hot Picks in Authors and Packages

nextGenShinyApps  
Craft Exceptional 'R Shiny' Applications and Dashboards with Novel Responsive Tools
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency  
multiocc  
Fits Multivariate Spatio-Temporal Occupancy Model
Spatio-temporal multivariate occupancy models can handle multiple species in occupancy models. This ...
Download / Learn more Package Citations See dependency  
stagePop  
Modelling the Population Dynamics of a Stage-Structured Species in Continuous Time
Provides facilities to implement and run population models of stage-structured species... ...
Download / Learn more Package Citations See dependency  
gclus  
Clustering Graphics
Orders panels in scatterplot matrices and parallel coordinate displays by some merit index. Package ...
Download / Learn more Package Citations See dependency  
CompoundEvents  
Statistical Modeling of Compound Events
Tools for extracting occurrences, assessing potential driving factors, predicting occurrences, and q ...
Download / Learn more Package Citations See dependency  
MatrixEQTL  
Matrix eQTL: Ultra Fast eQTL Analysis via Large Matrix Operations
Matrix eQTL is designed for fast eQTL analysis on large datasets. Matrix eQTL can test for associat ...
Download / Learn more Package Citations See dependency  

22,114

R Packages

188,080

Dependencies

55,244

Author Associations

22,115

Publication Badges

© Copyright 2022 - present. All right reserved, rpkg.net. Contact Us / Suggestions / Concerns