ttestClust {htestClust}

Test of Marginal Means in Clustered Data

Description

Performs one and two sample tests of marginal means in clustered data, reweighted to correct for potential cluster- or group-size informativeness.

Usage

ttestClust(x, ...)

## Default S3 method:
ttestClust(
  x,
  y = NULL,
  idx,
  idy = NULL,
  alternative = c("two.sided", "less", "greater"),
  mu = 0,
  paired = FALSE,
  conf.level = 0.95,
  ...
)

## S3 method for class 'formula'
ttestClust(formula, id, data, subset, na.action, ...)

Arguments

x, y

numeric vectors of data values.

...

further arguments to be passed to or from methods.

idx

vector or factor object denoting cluster membership for x observations (or cluster membership for paired observations when paired is TRUE). Length must be equal to length of x.

idy

vector or factor object denoting cluster membership for y observations. Length must be equal to length of y

alternative

indicates the alternative hypothesis and must be one of "two.sided", "greater", or "less".You can specify just the initial letter.

mu

a number specifying an optional parameter used to form the null hypothesis.

paired

a logical indicating whether x and y are paired.

conf.level

confidence level of the interval.

formula

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

id

a vector or factor giving the corresponding 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.

na.action

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

Details

The formula interface is only applicable for the 2-sample tests.

If paired is TRUE then x, y, and idx must be given and be of the same length. idy is ignored.

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.

conf.int

a confidence interval for the mean appropriate to the specified alternative hypothesis

estimate

the estimated mean or difference in means, depending on whether it was a one-sample or two-sample test.

null.value

the specified hypothesized value of the mean or mean difference.

alternative

a character string describing the alternative hypothesis.

method

a character string indicating what type of reweighted test of means was performed.

data.name

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

M

the number of clusters.

References

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

Examples

data(screen8)
## One sample test
## Test if marginal math scores are equal to 70
ttestClust(x=screen8$math, idx=screen8$sch.id, mu = 70)

## paired test
## Test equality of marginal means in math and reading scores
ttestClust(x=screen8$math, y=screen8$read, idx=screen8$sch.id, paired=TRUE)

## unpaired test
## Test if boys and girls have equal marginal math scores
boys <- subset(screen8, gender=='M')
girls <- subset(screen8, gender=='F')
ttestClust(x=boys$math, y=girls$math, idx=boys$sch.id, idy=girls$sch.id)

## unpaired test using formula method
ttestClust(math~gender, id=sch.id, data=screen8)