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Calibration weights via iterative proportional fitting (raking)

Source: R/sampling.R
morie_calibration_weights.Rd

Adjusts initial design weights so that weighted marginal totals match known population totals for each auxiliary variable.

Usage

morie_calibration_weights(
  df,
  aux_vars,
  population_totals,
  initial_weights = NULL,
  max_iter = 50L,
  tol = 1e-06
)

Arguments

df

A data frame.

aux_vars

Character vector of categorical auxiliary variable names.

population_totals

Named list: "var_level" -> population count. Keys should be "varname_level" (e.g. "gender_female").

initial_weights

Optional numeric vector of starting weights.

max_iter

Maximum IPF iterations.

tol

Convergence tolerance.

Value

Numeric vector of calibrated weights.

Examples

set.seed(1)
df <- data.frame(
  region = sample(c("A", "B"), 100, TRUE),
  sex = sample(c("M", "F"), 100, TRUE)
)
totals <- list(region_A = 60, region_B = 40, sex_M = 55, sex_F = 45)
morie_calibration_weights(df,
  aux_vars = c("region", "sex"),
  population_totals = totals
)
#>   [1] 1.1634999 0.7424349 1.1634999 1.2830401 0.8187142 1.2830401 1.2830401
#>   [8] 1.1634999 0.8187142 0.8187142 1.2830401 1.2830401 1.2830401 1.1634999
#>  [15] 1.1634999 0.7424349 0.7424349 0.8187142 0.7424349 1.1634999 1.1634999
#>  [22] 1.1634999 1.2830401 1.2830401 1.1634999 1.2830401 0.8187142 1.1634999
#>  [29] 1.1634999 0.8187142 0.8187142 0.8187142 1.1634999 0.8187142 1.1634999
#>  [36] 1.2830401 0.7424349 1.1634999 0.7424349 0.8187142 0.7424349 0.8187142
#>  [43] 1.2830401 0.7424349 0.8187142 0.7424349 0.7424349 0.7424349 1.2830401
#>  [50] 1.2830401 0.7424349 1.2830401 0.8187142 0.8187142 1.2830401 1.1634999
#>  [57] 0.7424349 0.7424349 0.7424349 1.1634999 1.2830401 0.8187142 0.8187142
#>  [64] 0.7424349 0.8187142 0.8187142 0.7424349 1.2830401 0.8187142 0.7424349
#>  [71] 0.8187142 0.8187142 1.1634999 1.2830401 1.2830401 0.7424349 0.7424349
#>  [78] 1.1634999 1.1634999 0.8187142 0.7424349 0.8187142 1.2830401 1.2830401
#>  [85] 1.2830401 0.8187142 1.1634999 0.8187142 1.1634999 0.8187142 1.2830401
#>  [92] 1.1634999 0.8187142 0.7424349 1.1634999 1.1634999 1.2830401 0.7424349
#>  [99] 0.8187142 1.2830401