bayesics (version 2.0.2)

heteroscedasticity_test: Test for heteroscedasticity in AOV models

Description

Use Chib's method to compute the Bayes factor to test for heteroscedasticity in analysis of variance models.

Usage

heteroscedasticity_test(hetero_model, homo_model)

Value

(returned invisible) A tibble with Bayes factors and interpretations.

Arguments

hetero_model

aov_b object where the heteroscedastic argument has been set to TRUE (the default)

homo_model

aov_b object where the heteroscedastic argument has been set to FALSE

References

Kass, R. E., & Raftery, A. E. (1995). Bayes Factors. Journal of the American Statistical Association, 90(430), 773–795.

Examples

# \donttest{
# Test homoscedastic case
## Generate some data
set.seed(2025)
N = 200
test_data = 
  data.frame(x1 = rep(letters[1:5],N/5))
test_data$outcome = 
  rnorm(N,-1 + 2 * (test_data$x1 %in% c("d","e")) )

## Fit the anova models
hetero_model = 
  aov_b(outcome ~ x1,
        test_data)
homo_model = 
  aov_b(outcome ~ x1,
        test_data,
        heteroscedastic = FALSE)

## Perform test for heteroscedasticity using Bayes factors
heteroscedasticity_test(hetero_model,
                        homo_model)

# Test heteroscedastic case
## Generate some data
set.seed(2025)
N = 200
test_data = 
  data.frame(x1 = rep(letters[1:5],N/5))
test_data$outcome = 
  rnorm(N,
        -1 + 2 * (test_data$x1 %in% c("d","e")),
        sd = 3 - 2 * (test_data$x1 %in% c("d","e")))

## Fit the anova models
hetero_model = 
  aov_b(outcome ~ x1,
        test_data)
homo_model = 
  aov_b(outcome ~ x1,
        test_data,
        heteroscedastic = FALSE)

## Perform test for heteroscedasticity using Bayes factors
heteroscedasticity_test(hetero_model,
                        homo_model)
# }