Implements a spatial extension of the random forest algorithm (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>) including a geographically weighted random forest (Georganos S, Kalogirou S. A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests. ISPRS International Journal of Geo-Information. 2022; 11(9):471. https://doi.org/10.3390/ijgi11090471).
Changelog
Version: 0.1.5 (2 September 2022)
grf: The function now supports Geographically Weighted Random Forest regression.
grf.bw: Geographically Weighted Random Forest optimal bandwidth selection
grf.mtry.optim: This function calculates the optimal mtry for a given Random Forest (RF) model in a specified range of values. The optimal mtry value can then be used in the grf model.
Version: 0.1.3 (9 May 2019)
grf: This function refers to a geographical (local) version of the popular Random Forest algorithm
predict.grf: Predict Method for Geographical Random Forest
random.test.data: Random data generator
Datasets
Income: Mean household income at local authorities in Greece in 2011
Download SpatialML
SpatialML is available at the Comprehensive R Archive Network (CRAN): https://CRAN.R-project.org/package=SpatialML. A reference manual for SpatialML is available here…
References
(2019) Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling, Geocarto International, DOI: 10.1080/10106049.2019.1595177