Variance Tests for Homogeneity

Compute and return information on tests for the homogeneity of data.

Usage

variance_tests(
  data,
  tests = c("ratio", "levene", "bartlett", "fligner"),
  ratio.threshold = 4
)

Arguments

data

Data.frame with the first column the values and the second column the group names

tests

Vector of strings, or a string, indicating the tests to check. Options include 'ratio', 'levene', 'bartlett', 'fligner', and 'hartley'

ratio.threshold

Numeric threshold for comparision of the variance ratio

Value

Lists of variances and desired variance tests

Details

Tests require independent, quantitative data unless otherwise specified. Ratio: Looks at the ratio of largest to smallest variance. Rule of thumb is that if this ratio is less than 4, no major violations are observed Levene: No other major assumptions Bartlett: Requires normally distributed data and no major outliers Fligner: Also called Fligner-Killeen. Non-parametric test Hartley: Requires normal distribution and equal number of observations.

See also

car::leveneTest(), stats::bartlett.test(), stats::fligner.test(), PMCMRplus::hartleyTest()

Examples

data <- data.frame(
  "value" = c(rnorm(14, sd = 2), rnorm(6), rnorm(20, mean = 2)),
  "group" = c(rep("A", 14), rep("B", 6), rep("C", 20))
)
variance_tests(data)
#> $var
#>        A        B        C 
#> 2.967607 1.033159 1.012913 
#> 
#> $ratio
#> [1] 2.929775
#> 
#> $ratio.threshold
#> [1] 4
#> 
#> $levene_pvalue
#> [1] 0.1270754
#> 
#> $bartlett_pvalue
#> [1] 0.08664783
#> 
#> $fligner_pvalue
#> [1] 0.006107502
#>