Package: waywiser 0.6.0
Michael Mahoney
waywiser:Ergonomic 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. Methods are provided for assessing models fit to spatial data, including approaches for measuring the spatial structure of model errors, assessing model predictions at multiple spatial scales, and evaluating where predictions can be made safely. Methods 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>), agreement coefficients from 'Ji' and Gallo (2006) (<doi:10.14358/PERS.72.7.823>), agreement metrics from 'Willmott' (1981) (<doi:10.1080/02723646.1981.10642213>) and 'Willmott' 'et' 'al'. (2012) (<doi:10.1002/joc.2419>), an implementation of the area of applicability methodology from 'Meyer' and 'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>), and an implementation of multi-scale assessment as described in 'Riemann' 'et' 'al'. (2010) (<doi:10.1016/j.rse.2010.05.010>).
Authors:
waywiser_0.6.0.tar.gz
waywiser_0.6.0.tar.gz(r-4.5-noble)waywiser_0.6.0.tar.gz(r-4.4-noble)
waywiser_0.6.0.tgz(r-4.4-emscripten)waywiser_0.6.0.tgz(r-4.3-emscripten)
waywiser.pdf |waywiser.html✨
waywiser/json (API)
NEWS
| # Installwaywiser in R: |
| install.packages('waywiser',repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ropensci/waywiser/issues
- guerry - Guerry "Moral Statistics"
- ny_trees - Number of trees and aboveground biomass for Forest Inventory and Analysis plots in New York State
- worldclim_simulation - Simulated data based on WorldClim Bioclimatic variables
Last updated 8 days agofrom:c5900624ed
Exports:ww_agreement_coefficientww_agreement_coefficient_vecww_area_of_applicabilityww_build_neighborsww_build_weightsww_global_geary_cww_global_geary_c_vecww_global_geary_pvalueww_global_geary_pvalue_vecww_global_moran_iww_global_moran_i_vecww_global_moran_pvalueww_global_moran_pvalue_vecww_local_geary_cww_local_geary_c_vecww_local_geary_pvalueww_local_geary_pvalue_vecww_local_getis_ord_gww_local_getis_ord_g_pvalueww_local_getis_ord_g_pvalue_vecww_local_getis_ord_g_vecww_local_moran_iww_local_moran_i_vecww_local_moran_pvalueww_local_moran_pvalue_vecww_make_point_neighborsww_make_polygon_neighborsww_multi_scaleww_systematic_agreement_coefficientww_systematic_agreement_coefficient_vecww_systematic_mpdww_systematic_mpd_vecww_systematic_mseww_systematic_mse_vecww_systematic_rmpdww_systematic_rmpd_vecww_systematic_rmseww_systematic_rmse_vecww_unsystematic_agreement_coefficientww_unsystematic_agreement_coefficient_vecww_unsystematic_mpdww_unsystematic_mpd_vecww_unsystematic_mseww_unsystematic_mse_vecww_unsystematic_rmpdww_unsystematic_rmpd_vecww_unsystematic_rmseww_unsystematic_rmse_vecww_willmott_dww_willmott_d_vecww_willmott_d1ww_willmott_d1_vecww_willmott_drww_willmott_dr_vec
Dependencies:bootclassclassIntcliDBIdeldirdotCall64dplyre1071fansifieldsFNNgenericsgluehardhatKernSmoothlatticelifecyclemagrittrmapsMASSMatrixpillarpkgconfigproxypurrrR6Rcpprlangs2sfspspamspDataspdeptibbletidyselectunitsutf8vctrsviridisLitewithrwkyardstick
Assessing models with waywiser
Rendered fromwaywiser.Rmdusingknitr::rmarkdownon Jun 28 2024.Last update: 2023-10-16
Started: 2023-03-19
Calculating residual spatial autocorrelation
Rendered fromresidual-autocorrelation.Rmdusingknitr::rmarkdownon Jun 28 2024.Last update: 2023-10-16
Started: 2023-03-19
Multi-scale model assessment
Rendered frommulti-scale-assessment.Rmdusingknitr::rmarkdownon Jun 28 2024.Last update: 2023-07-03
Started: 2023-03-19
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Guerry "Moral Statistics" (1830s) | guerry |
| Number of trees and aboveground biomass for Forest Inventory and Analysis plots in New York State | ny_trees |
| Predict from a 'ww_area_of_applicability' | predict.ww_area_of_applicability |
| Simulated data based on WorldClim Bioclimatic variables | worldclim_simulation |
| Agreement coefficients and related methods | ww_agreement_coefficient ww_agreement_coefficient.data.frame ww_agreement_coefficient_vec ww_systematic_agreement_coefficient ww_systematic_agreement_coefficient.data.frame ww_systematic_agreement_coefficient_vec ww_systematic_mpd ww_systematic_mpd.data.frame ww_systematic_mpd_vec ww_systematic_rmpd ww_systematic_rmpd.data.frame ww_systematic_rmpd_vec ww_unsystematic_agreement_coefficient ww_unsystematic_agreement_coefficient.data.frame ww_unsystematic_agreement_coefficient_vec ww_unsystematic_mpd ww_unsystematic_mpd.data.frame ww_unsystematic_mpd_vec ww_unsystematic_rmpd ww_unsystematic_rmpd.data.frame ww_unsystematic_rmpd_vec |
| Find the area of applicability | ww_area_of_applicability ww_area_of_applicability.data.frame ww_area_of_applicability.formula ww_area_of_applicability.matrix ww_area_of_applicability.recipe ww_area_of_applicability.rset |
| Make 'nb' objects from sf objects | ww_build_neighbors |
| Build "listw" objects of spatial weights | ww_build_weights |
| Global Geary's C statistic | ww_global_geary_c ww_global_geary_c_vec ww_global_geary_pvalue ww_global_geary_pvalue_vec |
| Global Moran's I statistic | ww_global_moran_i ww_global_moran_i_vec ww_global_moran_pvalue ww_global_moran_pvalue_vec |
| Local Geary's C statistic | ww_local_geary_c ww_local_geary_c_vec ww_local_geary_pvalue ww_local_geary_pvalue_vec |
| Local Getis-Ord G and G* statistic | ww_local_getis_ord_g ww_local_getis_ord_g_pvalue ww_local_getis_ord_g_pvalue_vec ww_local_getis_ord_g_vec |
| Local Moran's I statistic | ww_local_moran_i ww_local_moran_i_vec ww_local_moran_pvalue ww_local_moran_pvalue_vec |
| Make 'nb' objects from point geometries | ww_make_point_neighbors |
| Make 'nb' objects from polygon geometries | ww_make_polygon_neighbors |
| Evaluate metrics at multiple scales of aggregation | ww_multi_scale |
| Willmott's d and related values | ww_systematic_mse ww_systematic_mse.data.frame ww_systematic_mse_vec ww_systematic_rmse ww_systematic_rmse.data.frame ww_systematic_rmse_vec ww_unsystematic_mse ww_unsystematic_mse.data.frame ww_unsystematic_mse_vec ww_unsystematic_rmse ww_unsystematic_rmse.data.frame ww_unsystematic_rmse_vec ww_willmott_d ww_willmott_d.data.frame ww_willmott_d1 ww_willmott_d1.data.frame ww_willmott_d1_vec ww_willmott_dr ww_willmott_dr.data.frame ww_willmott_dr_vec ww_willmott_d_vec |
