Skip to contents

Ansari-Bradley Test of Homogeneity of Variances

Source: R/Ansari_Bradley_test.R
Ansari_Bradley_test.Rd

Performs Ansari-Bradley test to assess the null hypothesis that the variances are equal across all groups (samples) defined by the independent variable.

Usage

Ansari_Bradley_test(
  data,
  formula,
  alpha = 0.05,
  silent = FALSE,
  summary = FALSE,
  misc = FALSE
)

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.

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.

Details

This function is based on Conover et al. (1981) and uses an approximate chi-square test. Results may be inaccurate for small total sample sizes (N < 20) or when there are many tied values.

References

Ansari, A. R., & Bradley, R. A. (1960). Rank-sum tests for dispersions. The Annals of Mathematical Statistics, 31, 1174–1189.

Conover, W. J., Johnson, M. E., & Johnson, M. M. (1981). A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics, 23, 351–361. https://doi.org/10.1080/00401706.1981.10487680

See also

stats::ansari.test

Examples

df0 <- CYCB1[[1]]
out <- Ansari_Bradley_test(df0, cells ~ grp)
#> 
#> ---------------------------------------------
#>  Ansari-Bradley homogeneity of variance test
#> 
#>  Response: cells
#> 
#>  Chi-Square: 8.3518
#>  Chi-Square critical: 7.8147
#>  p-value: 0.03927
#> 
#>  #> Group variances are unequal. 
#> ---------------------------------------------
#> 
boxplot(cells ~ grp, df0, horizontal = TRUE)
points(x = df0$cells, y = jitter(as.numeric(df0$grp), amount = 0.15))