levene_test {cmstatr}

Levene's Test for Equality of Variance

Description

This function performs the Levene's test for equality of variance.

Usage

levene_test(data = NULL, x, groups, alpha = 0.05, modcv = FALSE)

Arguments

data

a data.frame

x

the variable in the data.frame or a vector on which to perform the Levene's test (usually strength)

groups

a variable in the data.frame that defines the groups

alpha

the significance level (default 0.05)

modcv

a logical value indicating whether the modified CV approach should be used.

Details

This function performs the Levene's test for equality of variance. The data is transformed as follows:

w_{ij} = \left| x_{ij} - m_i \right|

Where m_i is median of the ith group. An F-Test is then performed on the transformed data.

When modcv=TRUE, the data from each group is first transformed according to the modified coefficient of variation (CV) rules before performing Levene's test.

Value

Returns an object of class adk. This object has the following fields:

• call the expression used to call this function

• data the original data supplied by the user

• groups a vector of the groups used in the computation

• alpha the value of alpha specified

• modcv a logical value indicating whether the modified CV approach was used.

• n the total number of observations

• k the number of groups

• f the value of the F test statistic

• p the computed p-value

• reject_equal_variance a boolean value indicating whether the null hypothesis that all samples have the same variance is rejected

• modcv_transformed_data the data after the modified CV transformation

References

“Composite Materials Handbook, Volume 1. Polymer Matrix Composites Guideline for Characterization of Structural Materials,” SAE International, CMH-17-1G, Mar. 2012.

See Also

calc_cv_star()

transform_mod_cv()

Examples

library(dplyr)

carbon.fabric.2 %>%
  filter(test == "FC") %>%
  levene_test(strength, condition)
##
## Call:
## levene_test(data = ., x = strength, groups = condition)
##
## n = 91          k = 5
## F = 3.883818    p-value = 0.00600518
## Conclusion: Samples have unequal variance ( alpha = 0.05 )