Effect size calculations for heteroscedasticity diagnostics

Converts chi-squared statistics produced by heteroscedasticity tests into interpretable effect sizes such as Cramer's V, the phi coefficient, or an eta-squared analogue. The helper also provides qualitative magnitude descriptors and a brief interpretation string that can be surfaced to users.

Usage

rcalculateEffectSize(
  test_result,
  model,
  data,
  type = c("cramers_v", "phi", "eta_squared")
)

Arguments

test_result

Result object from a heteroscedasticity test (typically of class htest).

model

Fitted model supplied to the diagnostic.

data

Data frame containing the variables referenced in model.

type

Effect size metric to compute. Supported options are "cramers_v", "phi", and "eta_squared".

Value

A named list with elements effect_size, magnitude, practical_significance, interpretation, and type.

Examples

data(mtcars)
model <- lm(mpg ~ wt + cyl, data = mtcars)
result <- performWhiteTest(model, mtcars)
#> [INFO] Running White test
#> [INFO] White test completed: statistic = 8.0275 df = 5 p = 0.1547
rcalculateEffectSize(result, model, mtcars)
#> $effect_size
#> [1] 0.2239913
#> 
#> $magnitude
#> [1] "small"
#> 
#> $practical_significance
#> [1] FALSE
#> 
#> $interpretation
#> [1] "Effect size 0.224 (cramers_v) suggests limited practical impact."
#> 
#> $type
#> [1] "cramers_v"
#>