waywiser: Methods for Assessing Spatial Models
Assessing predictive models of spatial data can be challenging,
both because these models are typically built for extrapolating outside the
original region represented by training data and due to potential spatially
structured errors, with "hot spots" of higher than expected error
clustered geographically due to spatial structure in the underlying
data. These functions provide methods for measuring the spatial
structure of model errors and evaluating where predictions can be made
safely, and are particularly useful for models fit using the 'tidymodels'
framework. Methods include Moran's I
('Moran' (1950) <doi:10.2307/2332142>), Geary's C
('Geary' (1954) <doi:10.2307/2986645>), Getis-Ord's G
('Ord' and 'Getis' (1995) <doi:10.1111/j.1538-4632.1995.tb00912.x>),
as well as an implementation of the area of applicability methodology from
'Meyer' and 'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>).
| Version: |
0.2.0 |
| Depends: |
R (≥ 3.6) |
| Imports: |
fields, glue, hardhat, Matrix, purrr, rlang, rsample, sf, spdep, stats, tibble, yardstick |
| Suggests: |
applicable, covr, dplyr, ggplot2, recipes, sfdep, spatialsample, spelling, testthat (≥ 3.0.0), tidymodels, tidyr, vip |
| Published: |
2022-10-23 |
| Author: |
Michael Mahoney 
[aut, cre],
Lucas Johnson
[ctb],
RStudio [cph, fnd] |
| Maintainer: |
Michael Mahoney <mike.mahoney.218 at gmail.com> |
| BugReports: |
https://github.com/mikemahoney218/waywiser/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/mikemahoney218/waywiser,
https://mikemahoney218.github.io/waywiser/ |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README NEWS |
| CRAN checks: |
waywiser results |
Documentation:
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