Find discrepant variables and their aggregate discrepancy scores

Calculates discrepancy between the current weighted distribution and target distributions for each variable, then aggregates using the chosen method.

Usage

find_discrepant_vars(
  data,
  targets,
  weights,
  choosemethod = "total",
  na_method = c("exclude", "bucket")
)

Arguments

data

Data frame.

targets

Named list of named numeric target vectors (proportions).

weights

Numeric weight vector.

choosemethod

Method for aggregating per-category discrepancies. One of "total", "max", "average", "totalsquared", "maxsquared", "averagesquared".

na_method

How to handle NA values. "exclude" skips NA cases from that margin. "bucket" treats missing values as an implicit extra category.

Value

Named numeric vector of aggregate discrepancy per variable.

Examples

data <- data.frame(
  gender = sample(c('M', 'F'), 100, replace = TRUE, prob = c(0.6, 0.4)),
  age = sample(c('young', 'old'), 100, replace = TRUE, prob = c(0.7, 0.3))
)
targets <- list(
  gender = c(M = 0.5, F = 0.5),
  age = c(young = 0.6, old = 0.4)
)
find_discrepant_vars(data, targets, weights = rep(1, 100))
#> gender    age 
#>   0.00   0.16