# Prediction support, surface uncertainty, and contour uncertainty

    library(potentiomap)
    data("synthetic_wells")
    p <- ps_make_points(synthetic_wells[1:16, ], "x", "y", "gw_elevation",
                        "well_id", "EPSG:26916")
    fit <- suppressWarnings(ps_interpolate(p, methods = "OK", grid_res = 350,
                                          support = TRUE, return = "result"))

    fit$support$summary

    ##             support_class cells percent
    ## 1               supported    54 44.6281
    ## 2   outside_training_hull    67 55.3719
    ## 3 beyond_maximum_distance     0  0.0000
    ## 4            outside_mask     0  0.0000
    ## 5  prediction_unavailable     0  0.0000
    ## 6    multiple_limitations     0  0.0000

    u <- ps_surface_uncertainty(fit, approach = "kriging_variance")
    u$method_manifest

    ##   method         approach
    ## 1     OK kriging_variance
    ##                                                                                                             assumptions
    ## 1 Model-conditional kriging variance under the fitted trend and variogram; this is not total hydrogeologic uncertainty.
    ##   simulation_count seed
    ## 1                0    1

    band <- ps_contour_uncertainty(u, 168, method = "gaussian_pointwise",
                                   accept_gaussian = TRUE)
    band$level_manifest

    ##   level probability             method pointwise_band_area_m2
    ## 1   168         0.9 gaussian_pointwise                8207500
    ##   finite_realizations gaussian_assumption
    ## 1                   0                TRUE

Support categories are not confidence classes. Kriging variance is conditional on the fitted covariance model, and the contour product is pointwise rather than a simultaneous confidence region.