Create adjustment factors to match expansion data to target year

Create adjustment factors to align initial expansion data with target year using ACS and PUMS data. Use when expansion and target years differ in weighting pipelines.

Usage

create_ie_adjustment_data(settings)

Arguments

settings

list. Must include:

• acs_year — ACS reference year

• pums_year — PUMS target year

• acs_dataset — ACS dataset name

• study_unit — study unit type ("household" or "person") Modified by reference: no.

Value

data.table with columns:

• PUMA — PUMA identifier

• n_pums_yr — PUMS year household count

• ref_count_yr — ACS year household count

• adj_factor — adjustment factor Rows: one per PUMA. Keys: (PUMA).

Details

• Fetches ACS and PUMS data for specified years.

• Calculates adjustment factors at PUMA level for households.

• Handles household and person study units; errors if unsupported.

• Returns a copy; does not modify by reference.

• Assumes valid ACS/PUMS years and study unit type.

Settings

• acs_year (direct): ACS reference year. Default from settings.

• pums_year (direct): PUMS target year. Default from settings.

• acs_dataset (direct): ACS dataset name. Default from settings.

• study_unit (direct): Study unit type. Default from settings.

See also

• fetch_pums

• scripts/weighting/expansion_adjustment.R

Other weighting pipeline: aggregate_targets(), calc_day_weights(), calc_linked_trip_weights(), calc_person_weights(), calc_sample_plan_counts(), calc_tour_weights(), calc_trip_weights(), create_target_update_table(), format_income_bins(), get_day_groups(), get_income_broad(), get_income_broad_xwalk(), prepare_zone_groups(), update_daypat_seed(), update_daypat_targets(), update_income_broad_labels(), update_rate_report(), update_targets()

Examples

## Not run:
settings <- list(acs_year = 2019, pums_year = 2020, acs_dataset = "acs5", study_unit = "household")
create_ie_adjustment_data(settings)
#> Error in get("pops_dir", settings): object 'pops_dir' not found
## End(Not run)