Summarize an ipf_rake object
summary.ipf_rake.RdProduces a detailed summary including per-variable diagnostic tables showing target vs. achieved distributions.
Usage
# S3 method for class 'ipf_rake'
summary(object, ...)Value
Invisibly returns a list with convergence info, weight summary, design effect, and per-variable assessment tibbles.
Examples
data <- data.frame(
gender = sample(c('M', 'F'), 100, replace = TRUE, prob = c(0.6, 0.4))
)
targets <- list(gender = c(M = 0.5, F = 0.5))
result <- rake(data, targets)
summary(result)
#>
#> ── Raking Summary (ipf)
#> ────────────────────────────────────────────────────────────────────────────────
#> ✔ Converged in 1 iterations (max prop err = 4.44e-16)
#> ℹ No base weights (uniform)
#> ℹ Selection: type = "nolim", method = "total"
#> ℹ Missing handling: "exclude"
#> ℹ Variables raked: "gender"
#> ── Weight Summary ──────────────────────────────────────────────────────────────
#> Min: 0.8333 Q1: 0.8333 Median: 0.8333 Mean: 1 Q3: 1.25 Max: 1.25
#> SD: 0.2041 CV: 0.2041
#> ── Design Effect ───────────────────────────────────────────────────────────────
#> Deff: 1.0417 | Effective n: 96 / 100
#> ── Per-Variable Assessment ─────────────────────────────────────────────────────
#>
#> ── gender
#> # A tibble: 3 × 9
#> level target unweighted_n unweighted_pct weighted_n weighted_pct change_pct
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 M 0.5 60 0.6 50 0.500 -0.1000
#> 2 F 0.5 40 0.4 50 0.500 0.100
#> 3 Total 1 100 1 100 1.00 0.200
#> # ℹ 2 more variables: residual_disc <dbl>, original_disc <dbl>
#>