dummy <- dummy_data()
schema_pop <- proto_schema(dummy$pop, dummy$survey$place_type, type = "ideal")
schema_inc <- ssu_schema(
"<=1000" = c(-Inf, 1000),
"1001-2000" = c(1001, 2000),
"2001-3000" = c(2001, 3000),
"3001-4000" = c(3001, 4000),
">4000" = c(4001, Inf),
adj_threshold = TRUE
)
wt_strata(
dummy$survey,
weights = list(
place_type = dummy$pop,
income = dummy$income
),
psu = dummy$areas,
ssu = dummy$survey[c("place_type", "income")],
schema = list(
place_type = schema_pop,
income = schema_inc
)
)
#> Warning: ! Composite weight surfaces do not overlap for at least one SSU combination.
#> Falling back to the union of individual surfaces.
#> ℹ If this is not the desired behavior, you can try the following:
#> - Set `composition = "sequential"` and use relative schemas
#> - Broaden the schema thresholds
#> Simple feature collection with 50 features and 2 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -0.4926006 ymin: -0.472044 xmax: 4.496891 ymax: 4.406297
#> Projected CRS: PROJCRS["unknown",
#> BASEGEOGCRS["unknown",
#> DATUM["Unknown based on WGS 84 ellipsoid",
#> ELLIPSOID["WGS 84",6378137,298.257223563,
#> LENGTHUNIT["metre",1],
#> ID["EPSG",7030]]],
#> PRIMEM["Greenwich",0,
#> ANGLEUNIT["degree",0.0174532925199433],
#> ID["EPSG",8901]]],
#> CONVERSION["unknown",
#> METHOD["Transverse Mercator",
#> ID["EPSG",9807]],
#> PARAMETER["Latitude of natural origin",0,
#> ANGLEUNIT["degree",0.0174532925199433],
#> ID["EPSG",8801]],
#> PARAMETER["Longitude of natural origin",0,
#> ANGLEUNIT["degree",0.0174532925199433],
#> ID["EPSG",8802]],
#> PARAMETER["Scale factor at natural origin",1,
#> SCALEUNIT["unity",1],
#> ID["EPSG",8805]],
#> PARAMETER["False easting",0,
#> LENGTHUNIT["metre",1],
#> ID["EPSG",8806]],
#> PARAMETER["False northing",0,
#> LENGTHUNIT["metre",1],
#> ID["EPSG",8807]]],
#> CS[Cartesian,2],
#> AXIS["(E)",east,
#> ORDER[1],
#> LENGTHUNIT["metre",1,
#> ID["EPSG",9001]]],
#> AXIS["(N)",north,
#> ORDER[2],
#> LENGTHUNIT["metre",1,
#> ID["EPSG",9001]]]]
#> First 10 features:
#> place_type income geometry
#> 1 Village 3001-4000 POINT (3.778597 -0.471939)
#> 2 Village 3001-4000 POINT (3.946702 1.965987)
#> 3 Village 1001-2000 POINT (0.993637 0.2133973)
#> 4 City <=1000 POINT (0.6572084 -0.002222611)
#> 5 Village 2001-3000 POINT (1.070045 1.096263)
#> 6 Village <=1000 POINT (1.160284 -0.4039758)
#> 7 City <=1000 POINT (-0.4926006 -0.2102328)
#> 8 Town 3001-4000 POINT (4.180163 -0.001154389)
#> 9 Village >4000 POINT (3.490712 1.889183)
#> 10 City <=1000 POINT (0.9663935 0.232882)
