cmstatr (version 0.8.0)

levene_test: 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.

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

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.

References

<U+201C>Composite Materials Handbook, Volume 1. Polymer Matrix Composites Guideline for Characterization of Structural Materials,<U+201D> SAE International, CMH-17-1G, Mar. 2012.

See Also

calc_cv_star()

transform_mod_cv()

Examples

# NOT RUN {
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 )

# }