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Homogeneity of variance test

Source: R/check_var_equal.R
check_var_equal.Rd

A wrapper function for the homoscedasticity tests available in this package.

Usage

check_var_equal(
  data,
  formula,
  alpha = 0.05,
  silent = FALSE,
  summary = FALSE,
  method = "LV",
  ...
)

Arguments

data

A data frame or a list of numeric vectors.

formula

Formula (default: NULL). If data is a data frame, define the val ~ group.

alpha

Significance threshold, range from 0 to 1 (default: 0.05).

silent

A logical value. If FALSE (default), results are printed to the console. If TRUE, no output is printed.

summary

A logical value (default: FALSE). If TRUE, a summary table for the input data is returned.

method

Character (default: "LV"). Abbreviation specifying the normality test to perform. Available options are c("AB", "BL", "FK", "LG", "LV", "MBF", "OB", "OM").

...

Additional arguments passed to the selected test function.

Value

A list.

Details

The method argument specifies the statistical procedure used to assess whether group variances are equal.

Available methods are:

  • "AB": Ansari-Bradley test A rank-based nonparametric test for homogeneity of scale (dispersion) across groups. It is often used as an alternative to the F-test when data are non-normal. However, the Fligner–Killeen test is generally more robust and is recommended for this purpose.

  • "BL": Bartlett test A classical parametric test for assessing homoscedasticity across multiple groups. It is highly sensitive to outliers and deviations from normality. When the data are normally distributed and free of outliers, it is the most robust and powerful tests for equality of variances.

  • "FK": Fligner–Killeen test A rank-based nonparametric test for homogeneity of variances across groups. It is based on absolute deviations from the median and is highly robust to non-normality and outliers.

  • "LG": 't Lam's G test An extension of Cochran's C test used to evaluate the internal consistency of variances. Although primarily designed as a variance outlier detection method rather than a formal test of homoscedasticity (such as Levene's or Bartlett's test), it can be used informally to assess variance homogeneity across groups. Its robustness is particularly strong under normality and in the absence of outliers.

  • "LV": Levene's test A classical test for equality of variances based on an ANOVA framework. This implementation uses the median as the center (instead of the mean), making it more robust to non-normality and outliers.

  • "MBF": Brown–Forsythe test (modified by Mehrotra) A robust modification of Levene's test proposed by Brown and Forsythe. The degrees of freedom are further adjusted following Mehrotra's correction, improving performance under non-normality and in the presence of outliers.

  • "OB": O'Brien test A variance homogeneity test similar in spirit to Levene's test, based on transformed observations designed to reduce sensitivity to non-normality.

  • "OM": O'Neill–Mathews test A modified Levene-type procedure that uses a weighted least squares approach.

In all methods, the null hypothesis is that group variances are equal.

Examples

check_var_equal(roGFP[[1]], ro ~ grp, method = "LV")
#> 
#> ---------------------------------------
#> Levene's homogeneity of variance test
#> 
#> Response: ro
#> 
#>           DF   SS MS Fvalue pvalue
#> Group      3 0.01  0 3.1837 0.0325
#> Residuals 46 0.03  0              
#> Total     49 0.03                 
#> 
#> #> Group variances are unequal.
#> ---------------------------------------