Lam_G_test: 't Lam's G Test of Homogeneity of Variances

In varequal: Tests and Measures for Homoscedasticity

't Lam's G Test of Homogeneity of Variances

Description

Performs Lam's G test, an extension of Cochran's C test, to evaluate the internal consistency of variances. Although it is primarily a variance outlier test rather than a "true" homoscedasticity test such as Levene's test or Bartlett's test, it can still be used conceptually to assess the homogeneity of variances across groups.

Usage

Lam_G_test(
  data,
  formula,
  alpha = 0.05,
  silent = FALSE,
  summary = FALSE,
  misc = FALSE,
  alternative = c("two.sided", "less", "greater")
)

Arguments

data	A data frame containing the variables specified in the formula.
formula	A formula of the form DV ~ IV, where DV is the dependent (response) variable and IV is the independent (grouping) variable.
alpha	A numeric value specifying the significance level. Must be between 0 and 1. Default is 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.
misc	A logical value. If FALSE (default), only essential parameters are returned. If TRUE, additional auxiliary parameters are included in the output.
alternative	Character (default: "two.sided"). Specifies the alternative hypothesis. Available options are c("two.sided", "less", "greater").

Details

⁠Note:⁠ Under normally distributed data and moderate sample sizes (8 < n < 20), Lam's G test performs comparably to Bartlett's test. For small sample sizes (n < 8), it appears to outperform several alternative tests, providing a favorable balance between Type I and Type II error rates. In contrast, some alternative methods achieve lower Type I error rates at the cost of substantially higher Type II error rates. Note that this test is highly sensitive to outliers.

Value

A list containing the test statistics, p-value, degrees of freedom, and optionally a summary table and/or auxiliary parameters, depending on the values of summary and misc.

References

’T Lam, R. U. E. (2010). Scrutiny of variance results for outliers: Cochran’s test optimized. Analytica Chimica Acta, 659(1–2), 68–84. https://doi.org/10.1016/j.aca.2009.11.032

See Also

Brown_Forsythe_test

Examples

df0 <- roGFP[[1]]
out <- Lam_G_test(df0, ro ~ grp)
boxplot(ro ~ grp, df0, horizontal = TRUE)
points(x = df0$ro, y = jitter(as.numeric(df0$grp), amount = 0.15))

varequal documentation built on Sept. 5, 2026, 5:08 p.m.