coin (version 0.4-2)

MarginalHomogeneityTest: Marginal Homogeneity Test

Description

Testing marginal homogeneity in a complete block design.

Usage

## S3 method for class 'formula':
mh_test(formula, data, subset = NULL, \dots)
## S3 method for class 'table':
mh_test(object, ...)
## S3 method for class 'SymmetryProblem':
mh_test(object, distribution = c("asymptotic", "approximate"), ...)

Arguments

formula

a formula of the form y ~ x | block where y is a factor giving the data values and x a factor with two or more levels giving the corresponding replications. block is an optional factor (

data

an optional data frame containing the variables in the model formula.

subset

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

object

an object inheriting from class SymmetryProblem or a table with identical dimnames attributes.

distribution

a character, the null distribution of the test statistic can be approximated by its asymptotic distribution (asymptotic) or via Monte-Carlo resampling (approximate). Alternatively, the functions

...

further arguments to be passed to or from methods.

Value

• An object inheriting from class IndependenceTest with methods show, pvalue and statistic.

Details

The null hypothesis of independence of row and column totals is tested. The corresponding test for binary factors x and y is known as McNemar test.

Scores must be a list of length one (row and column scores coincide). When scores are given or if x is ordered, the corresponding linear association test is computed (see Agresti, 2002).

References

Alan Agresti (2002), Categorical Data Analysis. Hoboken, New Jersey: John Wiley & Sons.

Examples

### Opinions on Pre- and Extramarital Sex, Agresti (2002), page 421
opinions <- c("always wrong", "almost always wrong", 
              "wrong only sometimes", "not wrong at all")

PreExSex <- as.table(matrix(c(144, 33, 84, 126, 
                                2,  4, 14,  29, 
                                0,  2,  6,  25, 
                                0,  0,  1,  5), nrow = 4, 
                            dimnames = list(PremaritalSex = opinions,
                                            ExtramaritalSex = opinions)))

### treating response as nominal
mh_test(PreExSex)

### and as ordinal
mh_test(PreExSex, scores = list(response = 1:length(opinions)))