R: Levene's Test for Homogeneity of Variance

LeveneTest {DescTools} | R Documentation

## Levene's Test for Homogeneity of Variance

### Description

Computes Levene's test for homogeneity of variance across groups.

### Usage

    LeveneTest(y, ...)

    ## S3 method for class 'formula'
    LeveneTest(formula, data, ...)
    ## S3 method for class 'lm'
    LeveneTest(y, ...)
    ## Default S3 method:
    LeveneTest(y, group, center=median, ...)

### Arguments

`y` | response variable for the default method, or a `lm` or `formula` object. If `y` is a linear-model object or a formula, the variables on the right-hand-side of the model must all be factors and must be completely crossed.
`group` | factor defining groups.
`center` | The name of a function to compute the center of each group; `mean` gives the original Levene's test; the default, `median`, provides a more robust test (Brown-Forsythe-Test).
`formula` | a formula of the form `lhs ~ rhs` where `lhs` gives the data values and `rhs` the corresponding groups.
`data` | an optional matrix or data frame (or similar: see `model.frame`) containing the variables in the formula `formula`. By default the variables are taken from `environment(formula)`.
`...` | arguments to be passed down, e.g., `data` for the `formula` and `lm` methods; can also be used to pass arguments to the function given by `center` (e.g., `center=mean` and `trim=0.1` specify the 10% trimmed mean).

### Value

returns an object meant to be printed showing the results of the test.

### Note

This function was previously published as leveneTest() in the library(car) and has been integrated here without logical changes.

### Author(s)

John Fox jfox@mcmaster.ca; original generic version contributed by Derek Ogle
adapted from a response posted by Brian Ripley to the r-help email list.

### References

Fox, J. (2008) Applied Regression Analysis and Generalized Linear Models, Second Edition. Sage.

Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage.

### See Also

`fligner.test` for a rank-based (nonparametric) k-sample test for homogeneity of variances; `mood.test` for another rank-based two-sample test for a difference in scale parameters; `var.test` and `bartlett.test` for parametric tests for the homogeneity in variance.

`ansari_test` in package coin for exact and approximate conditional p-values for the Ansari-Bradley test, as well as different methods for handling ties.

### Examples

    ## example from ansari.test:
    ## Hollander & Wolfe (1973, p. 86f):
    ## Serum iron determination using Hyland control sera
    ramsay <- c(111, 107, 100, 99, 102, 106, 109, 108, 104, 99,
                101, 96, 97, 102, 107, 113, 116, 113, 110, 98)
    jung.parekh <- c(107, 108, 106, 98, 105, 103, 110, 105, 104,
                100, 96, 108, 103, 104, 114, 114, 113, 108, 106, 99)

    LeveneTest( c(ramsay, jung.parekh),
      factor(c(rep("ramsay",length(ramsay)), rep("jung.parekh",length(jung.parekh)))))

    LeveneTest( c(rnorm(10), rnorm(10, 0, 2)), factor(rep(c("A","B"),each=10)) )

    ## Not run:
    # original example from package car

    with(Moore, LeveneTest(conformity, fcategory))
    with(Moore, LeveneTest(conformity, interaction(fcategory, partner.status)))

    LeveneTest(conformity ~ fcategory * partner.status, data = Moore)
    LeveneTest(conformity ~ fcategory * partner.status, data = Moore, center = mean)
    LeveneTest(conformity ~ fcategory * partner.status, data = Moore, center = mean, trim = 0.1)

    LeveneTest(lm(conformity ~ fcategory*partner.status, data = Moore))

    ## End(Not run)

[Package DescTools version 0.99.35 Index]