htestClust (version 0.2.2)

levenetestClust: Reweighted Levene's Test for Homogeneity of Variance in Clustered Data

Description

Performs a reweighted test for homogeneity of marginal variances across intra-cluster groups in clustered data. Appropriate for clustered data with cluster- or group size informativeness.

Usage

levenetestClust(y, ...)
# S3 method for default
levenetestClust(y, group, id, center = c("median", "mean"), trim = NA, ...)
# S3 method for formula
levenetestClust(formula, id, data, subset, na.action, ...)

Arguments

y

vector of numeric responses.

...

further arguments to be passed to or from methods.

group

vector or factor object defining groups.

id

vector or factor object denoting cluster membership for y responses.

center

The name of a function to compute the center of each group. If mean, the reweighted group means will be used. The default median is the suggested measure of center, as it provides a more robust test.

trim

optional numeric argument taking values [0, 0.5] to specify the percentage trimmed mean. Ignored if center = median.

formula

a formula of the form lhs ~ rhs where lhs is a numeric variable giving the data values and rhs a factor with two or more levels giving the corresponding groups.

data

an optional matrix or data frame containing variables in the formula formula and id. By default the variables are taken from environment(formula).

subset

an optional vector specifying a subset of observations to be used.

na.action

a function which indicates what should happen when data contain NAs. Defaults to getOption("na.action").

Value

A list with class "htest" containing the following components:

statistic

the value of the test statistic.

p.value

the p-value of the test.

parameter

the degrees of freedom of the chi square distribution.

method

a character string indicating the test performed.

data.name

a character string giving the name of the data and the total number of clusters.

M

the number of clusters.

Details

The null hypothesis is that all levels of group have equal marginal variances.

References

Gregg, M., Marginal methods and software for clustered data with cluster- and group-size informativeness. PhD dissertation, University of Louisville, 2020.

Examples

# NOT RUN {
data(screen8)

## Do boys and girls have the same variability in math scores?
## Test using vectors
levenetestClust(y=screen8$math, group=screen8$gender, id=screen8$sch.id)

## Test using formula method
levenetestClust(math~gender, id=sch.id, data=screen8)

## Using 10% trimmed mean
levenetestClust(math~gender, id=sch.id, data=screen8, center="mean", trim=.1)

# }