This repo contains all the needed code and data to reproduce the paper SpICE: An interpretable method for spatial data under review in Computational Statistics Journal.
- data: contains data and intermediate output
- paper: manuscript and figures
- rcode_workflow: rstats code used to produce results and figures
- 01_dataset_map.R: code, data transformation and map
- 02_run_models.R: Code to run models
- 03_model_results.R: Code with model results
- 04_spice_clusters.R: Code for cluster ICE curves
- zapp_simStudy.R: Appendix, simulation study
- zapp_toyExample.R: Appendix, Toy example
Several R packages were used to produce paper's reults:
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General data manipulation and ploting: tidyverse, RColorBrewer, patchwork
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Machine learning models: h2o. H2O runs on Java. To build H2O or run H2O tests, the 64-bit JDK is required. To run the H2O binary using either the command line, R, or Python packages, only 64-bit JRE is required. H2O supports the following versions of Java: Java SE 17, 16, 15, 14, 13, 12, 11, 10, 9, 8. More details here: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/welcome.html
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Packages needed for smoothing and clustering ICE curves: KernSmooth, ClustGeo, sf
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Packages neede for maps and more ploting: leaflet, mapview, ggmap (an API key is required, check https://github.com/dkahle/ggmap)