FKTest: Fligner-Killeen Test for Homogeneity of Variances (Manual Implementation)

Description

Performs a non-parametric Fligner-Killeen test for equality of variances across two or more groups, using raw vectors via a formula interface.

Usage

FKTest(formula, data, alpha = 0.05)

Value

An object of class "homocedasticidad", containing:

Statistic: The Fligner-Killeen chi-squared statistic.
df: Degrees of freedom.
p_value: The p-value for the test.
Decision: "Homoscedastic" or "Heteroscedastic" depending on the test result.
Method: A string indicating the method used ("Fligner-Killeen").

Arguments

formula: A formula of the form y ~ group, where y is numeric and group is a grouping variable (factor or coercible to factor).
data: A data frame containing the variables in the formula.
alpha: Significance level (default is 0.05).

Details

This test is particularly useful when the assumption of normality is violated, as it is robust to outliers and distributional deviations. It serves as a reliable alternative to Bartlett’s test when data do not follow a normal distribution.

Advantages: - Non-parametric: No assumption of normality. - Robust to outliers. - Suitable for heterogeneous sample sizes.

Disadvantages: - Less powerful than parametric tests under normality. - May be computationally intensive with large datasets.

References

Fligner, M. A., & Killeen, T. J. (1976). "Distribution-free two-sample tests for scale." Journal of the American Statistical Association, 71(353), 210–213. <https://doi.org/10.1080/01621459.1976.10480351>

Examples

Run this code

data(d_e, package = "Analitica")
res <- FKTest(Sueldo_actual ~ labor, data = d_e)
summary(res)

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