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library(potentiomap)
data("synthetic_wells")
p <- ps_make_points(synthetic_wells[1:16, ], "x", "y", "gw_elevation",
"well_id", "EPSG:26916")Validation designs represent different prediction tasks. Spatial separation is appropriate when transfer to unsampled areas matters; random folds answer a different question.
v <- ps_validate(p, c("IDW", "TPS"), design = "kfold", folds = 3,
prediction_mode = "direct", seed = 12)## [inverse distance weighted interpolation]
## [inverse distance weighted interpolation]
## Warning:
## Grid searches over lambda (nugget and sill variances) with minima at the endpoints:
## (GCV) Generalized Cross-Validation
## minimum at right endpoint lambda = 0.0001771007 (eff. df= 9.500013 )
## [inverse distance weighted interpolation]## fold_id training_count validation_count
## xmin repeat_001_fold_2 11 5
## xmin1 repeat_001_fold_1 10 6
## xmin2 repeat_001_fold_3 11 5comparison <- ps_compare_methods(v, metric = "rmse")
comparison$ranking[, c("method", "rmse", "finite_coverage", "rank")]## method rmse finite_coverage rank
## 1 IDW 1.3198115 1 2
## 7 TPS 0.4293667 1 1These scores are conditional on the recorded folds; they are not universal method rankings or automatic map accuracy.
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