1. Introduction | 2. Quick start with synthetic data | 2.1 Tuning mtry globally | 2.2 Optimal bandwidth | 2.3 Fitting the GRF | 2.4 Inspecting the fit | 2.5 Predicting at new locations | 3. Real-world example: Greek municipal income | 4. Practical tips | References
Package: SpatialML 1.8.2
Stamatis Kalogirou
SpatialML: Spatial Machine Learning
Implements a spatial extension of the random forest algorithm (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>). Provides a Geographically Weighted Random Forest regression and a routine to find the optimal bandwidth (Georganos and Kalogirou (2022) <doi:10.3390/ijgi11090471>). A lightweight cross-validation helper for tuning the 'mtry' parameter of a random forest and a generator of synthetic spatial test data are also included. The package depends on 'ranger' as its single random-forest back-end.
Authors:
SpatialML_1.8.2.tar.gz
SpatialML_1.8.2.tar.gz(r-4.7-any)SpatialML_1.8.2.tar.gz(r-4.6-any)
SpatialML_1.8.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SpatialML/json (API)
| # Install 'SpatialML' in R: |
| install.packages('SpatialML', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- Income - Mean Household Income at the Local Authorities of Greece in 2011
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:c5991c81cd. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 135 | ||
| source / vignettes | OK | 193 | ||
| linux-release-x86_64 | OK | 136 | ||
| wasm-release | OK | 139 |
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Spatial Machine Learning: Geographically Weighted Random Forest | SpatialML-package SpatialML |
| Geographically Weighted Random Forest Model | grf |
| Optimal Bandwidth Selection for a Geographically Weighted Random Forest | grf.bw |
| Mean Household Income at the Local Authorities of Greece in 2011 | Income |
| Predict Method for a Geographically Weighted Random Forest | predict.grf |
| Random Data Generator | random.test.data |
| Optimal mtry for a Random Forest via OOB or Cross-Validation | rf.mtry.optim |