htestClust (version 0.2.2)

onewaytestClust: Test for Equal Marginal Means in Clustered Data

Description

Test whether two or more intra-cluster groups have the same marginal means in clustered data. Reweighted to correct for potential cluster- or group size informativeness.

Usage

onewaytestClust(x, ...)
# S3 method for default
onewaytestClust(x, ...)
# S3 method for formula
onewaytestClust(formula, id, data, subset, ...)

Arguments

x

a two-dimensional matrix or data frame containing the within-cluster group means, where rows are the clusters and columns are the group means.

...

further arguments to be passed to or from methods.

formula

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

id

a vector or factor object denoting cluster membership.

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.

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.

estimate

the estimated marginal group means.

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 means.

If x is a matrix or data frame, the dimension of x should be MxK, where M is the number of clusters and K is the number of groups. Each row of x corresponds to a cluster, where the column values contain the respective group means from that cluster. Clusters which do not contain observations in a particular group should have NA in the corresponding column.

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 average reading scores differ across after-school activities?
## test using a table
read.tab <- tapply(screen8$read, list(screen8$sch.id, screen8$activity), mean)
onewaytestClust(read.tab)

## test using formula method
onewaytestClust(read~activity, id=sch.id, data=screen8)

# }