DescTools (version 0.99.37)

LeveneTest: Levene's Test for Homogeneity of Variance

Description

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

Usage

LeveneTest(y, ...)
# S3 method for formula
LeveneTest(formula, data, ...)
# S3 method for lm
LeveneTest(y, ...)
# S3 method for default
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.

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

# NOT RUN {
## 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))
# }

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