Create long table for rake weighitng
rake_target(formula, poptable, newtable, area_var, count_var = "count")A representation of the aggregate imputation or "outcome" model,
of the form X_{K} ~ X_1 + ... X_{K - 1}
The population table, collapsed in terms of counts. Must contain
all variables in the RHS of formula, as well as the variables specified in
area_var and count_var below.
A dataset that contains marginal counts or proportions. Will be collapsed internally to get simple proportions.
A character vector of the area of interest.
A character variable that specifies which variable in poptable
indicates the count
A long table that, for each districts, list the target distribution
of the outcome (variable = "outcome") and the cell of the known Xs (variable = "Xs).
This is consistent with the dataframe format for targets in the
autumn package https://github.com/aaronrudkin/autumn
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
library(ccesMRPprep)
# suppose we want know the distribution of (age x female) and we know the
# distribution of (race), by CD, but we don't know the joint of the two.
race_target <- count(acs_race_NY, cd, race, wt = count, name = "count")
rake_target(race ~ age + female,
poptable = acs_race_NY,
newtable = race_target,
area_var = "cd")
#> # A tibble: 432 × 4
#> cd variable label proportion
#> <chr> <chr> <chr> <dbl>
#> 1 NY-01 Xs 18 to 24 years_0 0.0592
#> 2 NY-01 Xs 18 to 24 years_1 0.0565
#> 3 NY-01 Xs 25 to 34 years_0 0.0787
#> 4 NY-01 Xs 25 to 34 years_1 0.0734
#> 5 NY-01 Xs 35 to 44 years_0 0.0714
#> 6 NY-01 Xs 35 to 44 years_1 0.0733
#> 7 NY-01 Xs 45 to 64 years_0 0.182
#> 8 NY-01 Xs 45 to 64 years_1 0.181
#> 9 NY-01 Xs 65 years and over_0 0.0999
#> 10 NY-01 Xs 65 years and over_1 0.125
#> # … with 422 more rows